[ { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The brief peak in Apectodinium, AOM and low salinity dinoflagellate cysts (Deflandrea) at 2617.4 m (Fig. 7) indicate a sporadic episode of surface water freshening/eutrophication before the CIE, which is best explained by an increase in regional precipitation due to its rapid nature. A coincident reduction in I. hiatus swamp conifers indicates possible disturbance of nearby coastal environments possibly from flooding (see Section 4.5). An associated reduction in \u03b413C may have been caused by stratification of the North Sea from an enhanced halocline, trapping 12C-enriched organic carbon at depth. This scenario may also explain the other peaks in Apectodinium at 2619.6 and 2614.7 m (although see Section 4.1).", "measurement_extractions": [ { "quantity": "2617.4 m", "unit": "m", "measured_entity": "brief peak", "measured_property": null }, { "quantity": "2619.6 and 2614.7 m", "unit": "m", "measured_entity": "other peaks", "measured_property": null } ], "split": "test", "docId": "S0012821X12004384-1610", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Correspondence analysis (first two axes) for: (a) spores and pollen species (total counts per gram, axis 1=39% of total variance, axis 2=17% of total variance) and (b) dinoflagellate cysts (total counts per gram, axis 1=31% of total variance, axis 2=11% of total variance), in core 22/10a-4. Symbols indicate depths for each pollen assemblage (PA) and dinoflagellate cyst assemblage (DA), and their correspondingly most associated palynomorph species. Marked depths (red) indicate the samples with pre-CIE peaks in Apectodinium and \u03b413CTOC. Samples at 2619.60 m and 2614.71 m (see Fig. 4) plot close to post-CIE onset assemblages PA3 and PA4, and either represent ephemeral episodes of marine and vegetation changes to post-CIE onset type conditions, or possible misplaced samples during drilling operations core handling. Analysis carried out using the software of Hammer et al. (2005). (For interpretation of the references to color in this figure caption, the reader is referred to the web version of this article.)", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "axes", "measured_property": null }, { "quantity": "39%", "unit": "%", "measured_entity": "total variance", "measured_property": "axis 1" }, { "quantity": "17%", "unit": "%", "measured_entity": "total variance", "measured_property": "axis 2" }, { "quantity": "31%", "unit": "%", "measured_entity": "total variance", "measured_property": "axis 1" }, { "quantity": "11%", "unit": "%", "measured_entity": "total variance", "measured_property": "axis 2" }, { "quantity": "2619.60 m and 2614.71 m", "unit": "m", "measured_entity": "Samples", "measured_property": null } ], "split": "test", "docId": "S0012821X12004384-990", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Lithology in the CTBI includes interbedded shales, clays and mudstones, some of which are pyritiferous. The organic matter in the black shales is of marine origin, but includes a significant fraction of terrigenous material (Forster et al., 2008). The black shales are highly laminated and relatively undisturbed by bioturbation. The \u03b413Corg record is incomplete due to poor core recovery and low sample yield, but an excursion signifying OAE 2 is recorded: a 0.5\u2030 VPDB negative shift immediately precedes the 4\u2030 positive excursion, \u221227.7 to \u221223.7\u2030 (Fig. 2; Supplementary Material, Table 1f; Forster et al., 2008). The characteristic excursion spans \u223c2 m of finely interbedded shales and mudstones. Throughout OAE 2, the \u03b413Corg values fluctuate between \u223c\u221223.5\u2030 to \u221227.5\u2030. The maximum enrichment in the \u03b413Corg is at 1035.75 mbsf, \u223c3.52 m into OAE 2 (Forster et al., 2008).", "measurement_extractions": [ { "quantity": "0.5\u2030", "unit": "\u2030", "measured_entity": "OAE 2", "measured_property": "VPDB negative shift" }, { "quantity": "4\u2030", "unit": "\u2030", "measured_entity": "OAE 2", "measured_property": "positive excursion" }, { "quantity": "\u221227.7 to \u221223.7\u2030", "unit": "\u2030", "measured_entity": "OAE 2", "measured_property": "4\u2030 positive excursion" }, { "quantity": "\u223c2 m", "unit": "m", "measured_entity": "excursion", "measured_property": null }, { "quantity": "between \u223c\u221223.5\u2030 to \u221227.5\u2030", "unit": "\u2030", "measured_entity": "OAE 2", "measured_property": "\u03b413Corg values" }, { "quantity": "1035.75 mbsf", "unit": "mbsf", "measured_entity": "\u03b413Corg", "measured_property": "maximum enrichment" }, { "quantity": "\u223c3.52 m", "unit": "m", "measured_entity": "\u03b413Corg", "measured_property": "maximum enrichment" } ], "split": "test", "docId": "S0012821X13007309-1649", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Thermodynamic modelling using MTDATA software was undertaken in addition to pilot scale tests, in a bid to improve understanding of the likely compounds and associated phase formation of elements under carbonation and calcination conditions. Modelling was undertaken for 4 major elements (C, Ca, O, and H) and up to 9 minor elements (Ba, Cd, Cr, K, Mg, Ni, Sr, Ti, and Zn) based on those which are considered to be most volatile, have the greatest negative impact on the environment and health, and which were present in the highest quantities in the limestone as identified by ICP-MS analysis as outlined in Table 2, in order to investigate partitioning behaviour under pilot scale CO2 capture conditions. The multiphase module of MTDATA was used in the modelling, combined with the Scientific Group Thermodata Europe (SGTE) database, in order to predict compound/phase formation for the complete temperature range, from temperatures of 600 \u00b0C, up to 750 \u00b0C for carbonation and 1000 \u00b0C for calcination, in steps of 20 \u00b0C, and at atmospheric pressure. Molar quantities, based on ICP-MS analysis of the limestone, for each of the elements being investigated were entered into the model.", "measurement_extractions": [ { "quantity": "4", "unit": null, "measured_entity": "major elements", "measured_property": null }, { "quantity": "up to 9", "unit": null, "measured_entity": "minor elements", "measured_property": null }, { "quantity": "600 \u00b0C, up to 750 \u00b0C", "unit": "\u00b0C", "measured_entity": "modelling", "measured_property": "temperature range" }, { "quantity": "1000 \u00b0C", "unit": "\u00b0C", "measured_entity": "modelling", "measured_property": "temperature" }, { "quantity": "20 \u00b0C", "unit": "\u00b0C", "measured_entity": "modelling", "measured_property": "steps" } ], "split": "test", "docId": "S0016236113008041-3127", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "SEM\u2013EDS analysis of sorbent sampled from the carbonator after each test was undertaken to determine the percentage weight of the elemental species present. The results as shown in Fig. 8 confirm that increasing bed inventory generally increases the presence of Al, Fe and Cu. Al was identified in the unreacted sample at a weight% of 0.34, but this increased to similar values of 0.67% and 0.66% for 4.5 kg and 6 kg samples respectively, with a much higher value of 3.98% for 13 kg sample of sorbent. This increase in Al% weight with increasing sorbent mass may be because there is a greater amount of Al present originally, or because the limestone acts as a sorbent for Al, which is also considered the case for Fe and Cu. Further, Al is likely to form Ca aluminates which are important constituents of cement. The EDS results indicate similar quantities of C, O and Ca between tests, although values for the samples that had undergone testing are slightly lower for C and O, and slightly higher for Ca, compared to the unreacted limestone sample, implying that the small change was a result of chemical reactions. Although the values are small, a steady decrease in Si weight% was recorded with increasing sorbent mass, implying removal of Si from limestone in the capture process. However the lowest value was recorded for the unreacted limestone.", "measurement_extractions": [ { "quantity": "weight% of 0.34", "unit": "weight%", "measured_entity": "unreacted sample", "measured_property": "Al" }, { "quantity": "0.67%", "unit": "%", "measured_entity": "4.5 kg", "measured_property": "Al" }, { "quantity": "0.66%", "unit": "%", "measured_entity": "6 kg samples", "measured_property": "Al" }, { "quantity": "4.5 kg and 6 kg", "unit": "kg", "measured_entity": "samples", "measured_property": null }, { "quantity": "3.98%", "unit": "%", "measured_entity": "13 kg sample of sorbent", "measured_property": "Al" }, { "quantity": "13 kg", "unit": "kg", "measured_entity": "sample of sorbent", "measured_property": null } ], "split": "test", "docId": "S0016236113008041-3257", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Flybys E12 and E13 (middle and right panels of Fig. 2) were nearly identical in terms of the flyby geometry, directly above the north pole of the moon with a minimum distance of only 50 km. Cassini was slightly downstream of the moon during encounter E12 and slightly upstream during E13. The details of the measurements for those two flybys are shown in Fig. 4. Intensities of electrons with different energies and look directions are shown in detail in the upper four panels on a linear scale. The lower two panels show the measured local pitch angle (115\u00b0 during E12 and about 75\u00b0 for E13) of the low-energy telescope of MIMI/LEMMS and the azimuthal component of the measured magnetic field B\u03d5. We use sometimes the B\u03d5 component as an index of field-aligned current perturbations in the system. Such currents may exist in the Alfv\u00e9n-wing system or represent discontinuities as predicted by Saur et al. (2007). Discontinuities in B\u03d5 could be useful in identifying the location where Cassini was magnetically connected to the surface of Enceladus. The intensities for electrons with tens to hundreds keV energies (e.g. in channel C3) show a \u201cramp\u201d- or step-like depletion (marked by red and green areas in Fig. 4A) before or after the deep absorption signature. The electron intensity drops gradually or sometimes in two steps from magnetospheric levels to background levels. It is interesting that the sharp ramps are mostly seen in the sub-Saturn upstream and in the anti-Saturn downstream region. This may be coincidence and has to be checked during the upcoming encounters for consistency.", "measurement_extractions": [ { "quantity": "50 km", "unit": "km", "measured_entity": "Flybys E12 and E13", "measured_property": "minimum distance" }, { "quantity": "two", "unit": null, "measured_entity": "flybys", "measured_property": null }, { "quantity": "four", "unit": null, "measured_entity": "panels", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "panels", "measured_property": null }, { "quantity": "115\u00b0", "unit": "\u00b0", "measured_entity": "E12", "measured_property": "measured local pitch angle" }, { "quantity": "about 75\u00b0", "unit": "\u00b0", "measured_entity": "E13", "measured_property": "measured local pitch angle" }, { "quantity": "tens to hundreds keV", "unit": "keV", "measured_entity": "electrons", "measured_property": "energies" }, { "quantity": "two", "unit": null, "measured_entity": "steps", "measured_property": null } ], "split": "test", "docId": "S0019103511004994-1607", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We will illustrate our tests of Liouville\u2019s theorem using data for electrons with an energy E \u2248 90 keV and a pitch angle \u03b1 \u2248 170\u00b0 before they encounter Rhea. Calculations with data from different LEMMS channels give similar results. Calculations are done at constant first adiabatic invariant (\u03bc = Esin2\u03b1(E + 2mc2)/(2mc2B)). Conservation of the second invariant is questionable, since a bounce for 170\u00b0 pitch angle electrons takes about 15 s. We therefore assume, for simplicity, that the pitch angle is always the measured one, being \u03b1 \u2248 170\u00b0 throughout the flyby.", "measurement_extractions": [ { "quantity": "\u2248 90 keV", "unit": "keV", "measured_entity": "electrons", "measured_property": "energy E" }, { "quantity": "\u2248 170\u00b0", "unit": "\u00b0", "measured_entity": "electrons", "measured_property": "pitch angle \u03b1" }, { "quantity": "170\u00b0", "unit": "\u00b0", "measured_entity": "electrons", "measured_property": "pitch angle" }, { "quantity": "about 15 s", "unit": "s", "measured_entity": "a bounce for 170\u00b0 pitch angle electrons", "measured_property": null }, { "quantity": "\u2248 170\u00b0", "unit": "\u00b0", "measured_entity": "pitch", "measured_property": "angle" } ], "split": "test", "docId": "S0019103512002801-1781", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The use of the guiding center approximation is justified since the gyroradius of energetic electrons is much smaller than Rhea\u2019s diameter (15\u201335 km for electrons between 20 and 100 keV) or the scale size of the various macroscopic interaction features (wake, expansion fans). Furthermore, field parameters in the simulation are static.", "measurement_extractions": [ { "quantity": "15\u201335 km", "unit": "km", "measured_entity": "Rhea", "measured_property": "diameter" }, { "quantity": "between 20 and 100 keV", "unit": "keV", "measured_entity": "electrons", "measured_property": "energetic" } ], "split": "test", "docId": "S0019103512002801-2075", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Moore et al. (2010), in the light of additional Cassini RSS observations, revisited their k1\u2217 rate and concluded that the best fit between model and observations was obtained when multiplying the original reaction rate of Moses and Bass (2000) by a factor of 0.125 which, with a revised average base reaction rate (from k1 = 2 \u00d7 10\u22129 to k1 = 1 \u00d7 10\u22129 cm3 s\u22121) (Huestis, 2008), corresponds effectively to a reduction of the assumed volume mixing ratio of H2(\u03bd\u2a7e4) by a factor of 4 with respect to that assumed by Moses and Bass (2000). For a more detailed discussion see Moore et al. (2010) and Galand et al. (2011).", "measurement_extractions": [ { "quantity": "0.125", "unit": null, "measured_entity": "factor", "measured_property": null }, { "quantity": "from k1 = 2 \u00d7 10\u22129 to k1 = 1 \u00d7 10\u22129 cm3 s\u22121", "unit": "cm3 s\u22121", "measured_entity": "average base reaction rate", "measured_property": null }, { "quantity": "4", "unit": null, "measured_entity": "factor", "measured_property": null } ], "split": "test", "docId": "S0019103512003533-3908", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Having focused so far on simulations for specific high latitude magnetospheric forcing conditions, we now explore the parameter space of possible electric field and particle precipitation fluxes to examine the atmospheric sensitivity to magnetospheric forcing. Diurnally-averaged temperatures at the peak ionospheric density level (10\u22125 mbar) and latitude 78\u00b0 from simulations R1\u2013R18 (Table 1) are shown in the upper panel of Fig. 12 as a function of 10 keV electron energy flux and peak electric field strength. As discussed in Section 3.2 (and shown for R15 in Fig. 6) the temperatures may be regarded as representing to within \u00b150 K exospheric and H3+ temperatures. While the values are based on equinox simulations, we found seasonal differences to be insignificant, generating temperature changes of \u2a7d10 K. The bottom panel of Fig. 12 shows as a function of 10 keV electron energy flux and peak electric field strength the column emission rates of H3+ calculated from the vertical profiles of H3+ densities and temperatures of simulations R1\u2013R18.", "measurement_extractions": [ { "quantity": "10\u22125 mbar", "unit": "mbar", "measured_entity": "ionospheric", "measured_property": "density level" }, { "quantity": "78\u00b0", "unit": "\u00b0", "measured_entity": "latitude", "measured_property": null }, { "quantity": "10 keV", "unit": "keV", "measured_entity": "electron", "measured_property": "energy flux" }, { "quantity": "\u00b150 K", "unit": "K", "measured_entity": "exospheric and H3+", "measured_property": "temperatures" }, { "quantity": "\u2a7d10 K", "unit": "K", "measured_entity": "seasonal differences", "measured_property": "temperature changes" }, { "quantity": "10 keV", "unit": "keV", "measured_entity": "electron", "measured_property": "energy flux" } ], "split": "test", "docId": "S0019103512003533-5211", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The temperature changes with electron energy flux depend on the electric field strength that was set. For a moderate field strength of 80 mV m\u22121 the temperature is virtually constant when increasing the energy flux from 0.2 to 1.2 mW m\u22122, while at E = 100 mV m\u22121 it increases from \u223c550 K to 850 K. Thus we can make the more general statement that for low to moderate electric field strength Saturn\u2019s thermospheric temperatures are more responsive to changes in electric field strength than incident energetic electron flux of 10 keV particles. Temperatures are less responsive to changes in energy flux for soft (500 eV) electrons (not shown) as these do not penetrate deep enough into the atmosphere to significantly affect Pedersen and Hall conductances (Galand et al., 2011).", "measurement_extractions": [ { "quantity": "0.2 to 1.2 mW m\u22122", "unit": "mW m\u22122", "measured_entity": "electron", "measured_property": "energy flux" }, { "quantity": "80 mV m\u22121", "unit": "mV m\u22121", "measured_entity": "field strength", "measured_property": null }, { "quantity": "100 mV m\u22121", "unit": "mV m\u22121", "measured_entity": "field strength", "measured_property": null }, { "quantity": "increases from \u223c550 K to 850 K.", "unit": "K", "measured_entity": "Saturn\u2019s thermospheric", "measured_property": "temperature" }, { "quantity": "10 keV", "unit": "keV", "measured_entity": "particles", "measured_property": null }, { "quantity": "500 eV", "unit": "eV", "measured_entity": "electrons", "measured_property": null } ], "split": "test", "docId": "S0019103512003533-5300", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A further finding from the upper panel of Fig. 12 relates to possible restrictions on combinations of electric field strength and 10 keV electron energy flux. The bottom left half of the figure (below the thick red line) represents a range of observed temperatures on Saturn (400\u2013650 K) and thus of \u201callowed\u201d combinations of electric field strength and particle flux. In contrast, combinations of these two magnetospheric forcing parameters that result in temperatures in the top right part of the figure (above the red line) need to be treated with caution as they produce temperatures in excess of observations. A magnetospheric electric field of \u223c100 mV m\u22121 mapped into the ionosphere would in combination with at 10 keV electron flux of 1 mW m\u22122 generate thermosphere temperatures of \u223c800 K, well in excess of observed values. This combination of values cannot thus occur for extended periods on Saturn.", "measurement_extractions": [ { "quantity": "10 keV", "unit": "keV", "measured_entity": "electron", "measured_property": "energy flux" }, { "quantity": "400\u2013650 K", "unit": "K", "measured_entity": "Saturn", "measured_property": "observed temperatures" }, { "quantity": "two", "unit": null, "measured_entity": "magnetospheric forcing parameters", "measured_property": null }, { "quantity": "\u223c100 mV m\u22121", "unit": "mV m\u22121", "measured_entity": "magnetospheric electric field", "measured_property": null }, { "quantity": "10 keV", "unit": "keV", "measured_entity": "electron", "measured_property": null }, { "quantity": "1 mW m\u22122", "unit": "mW m\u22122", "measured_entity": "10 keV electron", "measured_property": "flux" }, { "quantity": "\u223c800 K", "unit": "K", "measured_entity": "thermosphere", "measured_property": "temperatures" } ], "split": "test", "docId": "S0019103512003533-5318", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(a) The C II 1334.5 \u00c5 line of HD209458 (solid line, Linsky et al., 2010) fitted with a Voigt profile (see Table 1) and adjusted for absorption by the ISM (dotted line). We assumed that the column density of ground state C+ in the ISM is 2.23 \u00d7 1019 m\u22122. The relative velocity of the ISM with respect to Earth is \u22126.6 km s\u22121 and the effective thermal velocity along the LOS to the star is 12.3 km s\u22121 (Wood et al., 2005). (b) The C II 1335.7 \u00c5 line of HD209458 fitted with a Voigt profile. Absorption by the ISM was assumed to be negligible. The model profiles were convolved to a spectral resolution of R = 17,500.", "measurement_extractions": [ { "quantity": "2.23 \u00d7 1019 m\u22122", "unit": "m\u22122", "measured_entity": "ground state C+ in the ISM", "measured_property": "column density" }, { "quantity": "\u22126.6 km s\u22121", "unit": "km s\u22121", "measured_entity": "ISM", "measured_property": "relative velocity" }, { "quantity": "12.3 km s\u22121", "unit": "km s\u22121", "measured_entity": "along the LOS to the star", "measured_property": "effective thermal velocity" }, { "quantity": "17,500", "unit": "R", "measured_entity": "spectral resolution", "measured_property": null } ], "split": "test", "docId": "S0019103512003995-1237", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "K10 demonstrated that absorption by hydrogen in the extended thermosphere of HD209458b explains the transits in the H Lyman \u03b1 line, and used the observations to constrain the mean temperature and composition of the thermosphere. They fitted both the line-integrated transit depth based on the low resolution G140L data (Vidal-Madjar et al., 2004; Ben-Jaffel and Hosseini, 2010), and the transit depths and light curve based on the medium resolution G140M data (Vidal-Madjar et al., 2003; Ben-Jaffel, 2007, 2008) (see Figs. 5 and 6 of K10 for the results). The results imply that the lower boundary of the absorbing layer of H is at p0 = 0.1\u20131 \u03bcbar, the mean temperature within the layer is T\u00af=8000\u201311,000K, and the upper boundary is at r\u221e = 2.7Rp. Recent photochemical calculations imply that H2 dissociates near the 1 \u03bcbar level (e.g., Paper I, Moses et al., 2011), and this is also supported by an observational lower limit for the vertical column density of H (France et al., 2010). Hence the mean temperature in the thermosphere of HD209458b is approximately 8250 K (the M7 model of K10).", "measurement_extractions": [ { "quantity": "0.1\u20131 \u03bcbar", "unit": "\u03bcbar", "measured_entity": "absorbing layer of H", "measured_property": "lower boundary" }, { "quantity": "8000\u201311,000K", "unit": "K", "measured_entity": "absorbing layer of H", "measured_property": "mean temperature" }, { "quantity": "2.7Rp", "unit": "Rp", "measured_entity": "absorbing layer of H", "measured_property": "upper boundary" }, { "quantity": "near the 1 \u03bcbar", "unit": "\u03bcbar", "measured_entity": "H2", "measured_property": "dissociates" }, { "quantity": "approximately 8250 K", "unit": "K", "measured_entity": "thermosphere of HD209458b", "measured_property": "mean temperature" } ], "split": "test", "docId": "S0019103512003995-2096", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 4 shows the density profiles of H, H+, O, and O+ from the C2 model, and the density profile of H from the M7b model. The difference between the transit depths based on the empirical and hydrodynamic models arises because the C2 model has large temperature gradients (Paper I) and a gradual H/H+ transition rather than a sharp cutoff. The difference does not arise because the density profile of the C2 model deviates from hydrostatic equilibrium. Given the temperature gradient in the model, the density profile is almost exactly in hydrostatic equilibrium below 3Rp. In fact, the neutral density profile of the C2 model is better represented by a mean temperature of 6300 K (not shown). This implies that the correspondence of the mean temperature of the empirical model and the pressure averaged temperature of the hydrodynamic model is relatively good but not exact.", "measurement_extractions": [ { "quantity": "below 3Rp", "unit": "3Rp", "measured_entity": "density profile", "measured_property": "hydrostatic equilibrium" }, { "quantity": "6300 K", "unit": "K", "measured_entity": "neutral density profile of the C2 model", "measured_property": "mean temperature" } ], "split": "test", "docId": "S0019103512003995-2579", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Salts are relevant to the habitability of Mars. First, perchlorate reduction is a metabolism used by some terrestrial bacteria in anaerobic conditions (Coates and Achenbach, 2004). If such organisms exist (or did exist) on Mars, then they could gain energy by reducing perchlorate and oxidizing an electron donor such as organic carbon or ferrous iron. This would require organic molecules to be present on Mars. Indigenous organic molecules have yet to be confirmed on Mars; however the presence of perchlorate itself may have inhibited the detection of organics on Mars in pyrolysis experiments (Navarro-Gonzalez et al., 2010). Second, perchlorate salts are highly deliquescent and significantly lower the freezing point of liquid water (Gough et al., 2011). The eutectic point of Mg(ClO4)2 is \u221257 \u00b0C (Stillman and Grimm, 2011), while that of Ca(ClO4)2 is \u221275 \u00b0C (Pestova et al., 2005). Given typical soil salt concentrations, small amounts of water (\u223c0.02 g H2O per g soil) would permit a water activity sufficient for terrestrial life to be viable (Kounaves et al., 2010b). Third, if all organisms require fixed nitrogen in proteins and nucleic acids, as they do on Earth, then the discovery of nitrogen oxyanions on Mars would be significant as well.", "measurement_extractions": [ { "quantity": "\u221257 \u00b0C", "unit": "\u00b0C", "measured_entity": "Mg(ClO4)2", "measured_property": "eutectic point" }, { "quantity": "\u221275 \u00b0C", "unit": "\u00b0C", "measured_entity": "Ca(ClO4)2", "measured_property": "eutectic point" }, { "quantity": "\u223c0.02 g", "unit": "g", "measured_entity": "g soil", "measured_property": "H2O" } ], "split": "test", "docId": "S0019103513005058-3189", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The purpose of the sensitivity tests was not to model a physically plausible environment on Mars; however, the tests revealed that no one factor could perturb the nominal model to produce amounts of perchlorate that approach the amount measured by the Phoenix Lander. A combination of such factors, including reduced odd nitrogen fluxes, higher oxygen mixing ratios, and increased temperatures may have worked in concert to produce larger perchlorate:nitrate ratios. Specifically, if odd nitrogen downward fluxes and oxygen escape rates are smaller than currently thought, nitrate fluxes would drop while perchlorate fluxes would increase. In conjunction, as the surface temperature warms toward 280 K (as it does in the current equatorial summer) local perchlorate production fluxes could increase further. While it is possible to tweak the photochemical model into this mode, it is difficult to imagine a self-consistent scenario where all of these processes are working in concert to create high perchlorate:nitrate and perchlorate:chloride ratios on Mars, so we favor an additional source for perchlorate.", "measurement_extractions": [ { "quantity": "toward 280 K", "unit": "K", "measured_entity": "Mars", "measured_property": "surface temperature" } ], "split": "test", "docId": "S0019103513005058-4499", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In addition to the topography images, the Peak Force tapping AFM measurements also provide information on nano-scale mechanical properties of the surface, i.e., deformation, adhesion, dissipation, and elastic modulus. The images in Fig. 5 show the nano-mechanical properties of the different surfaces, obtained simultaneously with the topography images in Fig. 4, respectively. The calculated mean values of each mechanical property image for the adsorbed Mefp-1 films before and after exposure to the FeCl3 solution are provided in Table 2. The property images in Fig. 5 and mean values in Table 2 show that the Fe3+ enhanced complexation of the adsorbed Mefp-1 film results in less deformation, a slightly lower adhesion force between the tip and the adsorbed protein film, and a lower energy dissipation as the tip is tapping over the surface. The DMT modulus data are fitted values from curves obtained as the tip is retracted after the initial deformation, which is a measure of the surface hardness. The results clearly show that Fe3+ complexation of the adsorbed Mefp-1 film results in a large increase in the modulus (average value from 38 MPa to 185 MPa).", "measurement_extractions": [ { "quantity": "38 MPa to 185 MPa", "unit": "MPa", "measured_entity": "surface hardness", "measured_property": "modulus" } ], "split": "test", "docId": "S0021979713004438-2004", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Watchdog timers. For each state s\u2208S, there can be watchdog timers (T,s,C) at node i, where T\u2208R+ is the duration of the timer and C is a clock. The watchdog timer has input port Si,i and a binary output port TimeT,s,C. The timer's module specification \u03a6Time is defined as follows. Let Ein be an execution of the timer's input port Si,i and Eout an execution of its output port TimeT,s,C. Then Eout\u2208\u03a6Time(Ein), iff the following holds:\u2013(Clock) Clock C is correct at all times, i.e., t\u2032\u2212t\u2a7dC(t\u2032)\u2212C(t)\u2a7d\u03d1(t\u2032\u2212t) for all t,t\u2032\u2208R, t\u03a9M. We note that energy dissipated via Joule heating also decreases slightly due to the small decrease in flow shear. Overall, then, the total power per hemisphere in case EF is only 30% that of case ES.", "measurement_extractions": [ { "quantity": "~6\u00d7", "unit": null, "measured_entity": "transient magnetospheric expansion event", "measured_property": "power dissipated in the atmosphere due to Joule heating" }, { "quantity": "~3\u00d7", "unit": null, "measured_entity": "transient magnetospheric expansion event", "measured_property": "power dissipated in the atmosphere" }, { "quantity": "~7\u00d7", "unit": null, "measured_entity": "accelerate the magnetosphere towards corotation", "measured_property": "power used" }, { "quantity": "~2600TW", "unit": "TW", "measured_entity": "hemisphere", "measured_property": "total power" }, { "quantity": "three times larger", "unit": null, "measured_entity": "total power", "measured_property": "hemisphere" }, { "quantity": "~75%", "unit": "%", "measured_entity": "\u2018magnetospheric\u2019 power", "measured_property": "magnitude" }, { "quantity": "30%", "unit": "%", "measured_entity": "ES", "measured_property": "total power per hemisphere in case EF" } ], "split": "test", "docId": "S0032063313003218-7156", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "We investigated the effect of transient variations in solar wind dynamic pressure on the M\u2013I coupling currents, thermospheric flows, heating and cooling rates and aurora of the Jovian system. We considered two scenarios: (i) a transient compression event and (ii) a transient expansion event. Both of these were imposed over a time scale of 3 h. A transient compression event consists of an initially expanded, steady-state magnetospheric configuration. The model Jovian magnetosphere then encounters a shock in the solar wind, which compresses the system. As the conceptual shock propagates past the magnetosphere, a rarefaction region follows and the magnetosphere subsequently expands back to its initial state. The opposite occurs for our expansion event.", "measurement_extractions": [ { "quantity": "3 h", "unit": "h", "measured_entity": "two scenarios", "measured_property": "time scale" } ], "split": "test", "docId": "S0032063313003218-7389", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "FEG-SEM images of the fracture surface of the epoxy polymers modified with (a) 10 wt%, and (b) 20 wt% of S-CSR particles at 20 \u00b0C.", "measurement_extractions": [ { "quantity": "10 wt%", "unit": "wt%", "measured_entity": "epoxy polymers", "measured_property": "S-CSR particles at 20 \u00b0C" }, { "quantity": "20 wt%", "unit": "wt%", "measured_entity": "epoxy polymers", "measured_property": "S-CSR particles at 20 \u00b0" }, { "quantity": "20 \u00b0C", "unit": "\u00b0C", "measured_entity": "epoxy polymers", "measured_property": "modified" } ], "split": "test", "docId": "S0032386113005454-1245", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Glass transition temperature, Tg, tensile Young's modulus, E, and fracture stress, \u03c3f, fracture energy, GIc, and fracture toughness, KIc, for the unmodified and S-CSR-modified epoxy polymers at 20 \u00b0C.", "measurement_extractions": [ { "quantity": "20 \u00b0C", "unit": "\u00b0C", "measured_entity": "unmodified and S-CSR-modified epoxy polymers", "measured_property": null } ], "split": "test", "docId": "S0032386113005454-1270", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Epoxy polymers are a class of high-performance thermosetting polymers which are widely used for the matrices of fibre-reinforced composite materials and as adhesives. They are known for their excellent engineering properties, such as high modulus, low creep, high strength, and good thermal and dimensional stabilities. However, epoxy polymers have inherently low toughness and impact resistance due to their highly crosslinked structure. This structure leads to brittle behaviour and causes the polymers to suffer from relatively poor resistance to crack initiation and growth. To improve the toughness of epoxy polymers, it has been established that the incorporation of a second micro-phase of a dispersed rubber, e.g. Refs. [1\u20135], or a thermoplastic polymer, e.g. Ref. [6\u20138], can increase the toughness. Here the rubber or thermoplastic particles are typically about 0.1\u20135 \u03bcm in diameter with a volume fraction of about 5\u201320%. The particles are typically well-dispersed, and formed by reaction-induced phase-separation. However, the particle size is difficult to control as it is dependent on the curing conditions, and hence cannot be varied systematically without changing the properties of the epoxy polymer.", "measurement_extractions": [ { "quantity": "about 0.1\u20135 \u03bcm", "unit": "\u03bcm", "measured_entity": "rubber or thermoplastic particles", "measured_property": "diameter" }, { "quantity": "about 5\u201320%", "unit": "%", "measured_entity": "rubber or thermoplastic particles", "measured_property": "volume fraction" } ], "split": "test", "docId": "S0032386113005454-2022", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Shear-band yielding has previously been reported for the present epoxy-polymer formulation when particle modified [33], and was observed during the present plane\u2013strain compression tests. The DN-4PB tests of the S-CSR-modified epoxy polymers were performed to investigate the process further. After fracture at 20 \u00b0C, the plastic zone at the tip of the sub-critically loaded crack was sectioned and observed using transmission optical microscopy. This showed that a large feather-like deformation zone was formed, see Fig. 16. This feather-like zone comprises highly plastically dilated cavities and localised shear-bands [12,48]. Transmission optical micrographs of the subsurface damage zone of the S-CSR particle-modified epoxy polymers tested at different temperatures revealed that the size of the subsurface damage zone decreased as the test temperature decreased, see Fig. 17. This reduction in the size of the deformation zone ahead of the crack tip is due to the increase of the yield stress of the epoxy polymer at low temperatures.", "measurement_extractions": [ { "quantity": "20 \u00b0C", "unit": "\u00b0C", "measured_entity": "S-CSR-modified epoxy polymers", "measured_property": "fracture" } ], "split": "test", "docId": "S0032386113005454-2876", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The toughening events of the S-CSR particle-modified epoxy polymers can be summarised as localised shear-banding of the epoxy polymer initiated by the particles and internal cavitation of the S-CSR particles (which relieves the triaxial stress-state in the vicinity of the crack tip) followed by void growth. These events contribute to the relatively high toughness measured from the S-CSR-modified epoxy polymers. The differences observed at low temperatures indicate that the increase of the cavitational resistance of the S-CSR particles delays the cavitation process and enhances the localised shear-band yielding process. The increase of the yield stress of the epoxy polymer attenuates the deformation of the polymer and reduces the size of the deformation zone ahead of the crack tip, but this effect is compensated for by the increased energy absorption from the enhanced shear-band yielding. Hence, there is a reduction of the toughening performance of the S-CSR particles at low temperatures, but the competition between these effects results in the fracture energy being independent of the test temperature between \u221255 \u00b0C and \u2212109 \u00b0C.", "measurement_extractions": [ { "quantity": "between \u221255 \u00b0C and \u2212109 \u00b0C", "unit": "\u00b0C", "measured_entity": "test", "measured_property": "temperature" } ], "split": "test", "docId": "S0032386113005454-2886", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In order to test the operation of the dual-chambers, measurements were made in a glasshouse using a mesocosm of 150 cm long, 100 cm wide, filled with a soil depth of 15 cm. The mesocosm had been sown with Lolium perenne (perennial rye-grass) the previous year and kept in a glasshouse under optimal growing conditions for approximately four months. Approximately 15 min before the beginning of the experiment, the aboveground vegetation was removed under the dual-chamber, as well as the reference chamber which was deployed at 30 cm distance from the dual-chamber. The CRDS continuous measurements of \u03b413C\u2013CO2, and CO2 concentration (Cb) from the bottom-chamber, started before deploying the dual-chamber on the ground.", "measurement_extractions": [ { "quantity": "150 cm", "unit": "cm", "measured_entity": "mesocosm", "measured_property": "long" }, { "quantity": "100 cm", "unit": "cm", "measured_entity": "mesocosm", "measured_property": "wide" }, { "quantity": "15 cm", "unit": "cm", "measured_entity": "mesocosm", "measured_property": "soil depth" }, { "quantity": "approximately four months", "unit": "months", "measured_entity": "mesocosm", "measured_property": "kept in a glasshouse" }, { "quantity": "Approximately 15 min", "unit": "min", "measured_entity": "aboveground vegetation", "measured_property": "removed under the dual-chamber" }, { "quantity": "30 cm", "unit": "cm", "measured_entity": "reference chamber", "measured_property": "distance from the dual-chamber" } ], "split": "test", "docId": "S0038071711004354-1624", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "At the site the mean annual temperature is 14.8 \u00b0C, the mean annual precipitation is 898 mm, and the seasonality over the year is characterized by a wet, mild winter and an arid, hot summer. The soil is a sandy calcareous regosoil, characterized by an O horizon with fresh and decaying organic matter 2\u20135 cm thickness, and a soil texture in the uppermost 0.1 m of mineral soil characterized by 93% sand, 4% clay, and 3% silt.", "measurement_extractions": [ { "quantity": "14.8 \u00b0C", "unit": "\u00b0C", "measured_entity": "site", "measured_property": "mean annual temperature" }, { "quantity": "898 mm", "unit": "mm", "measured_entity": "site", "measured_property": "mean annual precipitation" }, { "quantity": "2\u20135 cm", "unit": "cm", "measured_entity": "O horizon with fresh and decaying organic matter", "measured_property": "thickness" }, { "quantity": "uppermost 0.1 m", "unit": "m", "measured_entity": "mineral soil", "measured_property": "soil texture" }, { "quantity": "93%", "unit": "%", "measured_entity": "uppermost 0.1 m of mineral soil", "measured_property": "sand" }, { "quantity": "4%", "unit": "%", "measured_entity": "uppermost 0.1 m of mineral soil", "measured_property": "clay" }, { "quantity": "3%", "unit": "%", "measured_entity": "uppermost 0.1 m of mineral soil", "measured_property": "silt" } ], "split": "test", "docId": "S0038071711004354-1644", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As for RS, fR, fL, and fSOM did not show a significant difference between the morning and afternoon measurements. Overall, fL showed the highest variability, with a SE in the afternoon (15%, \u00b1t0.05,d\u0192 = 14\u201375%), while fSOM showed the lowest SEs (5%, \u00b1t0.05,d\u0192 = 27\u201348%).", "measurement_extractions": [ { "quantity": "15%, \u00b1t0.05", "unit": "%", "measured_entity": "fL", "measured_property": "SE" }, { "quantity": "14\u201375%", "unit": "%", "measured_entity": "SE", "measured_property": "d\u0192" }, { "quantity": "5%, \u00b1t0.05", "unit": "%", "measured_entity": "fSOM", "measured_property": "SEs" }, { "quantity": "27\u201348%", "unit": "%", "measured_entity": "SEs", "measured_property": "d\u0192" } ], "split": "test", "docId": "S0038071711004354-2370", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The partitioning obtained using the two end-member mixing model suggested that the autotrophic flux of 0.18 g CO2 m\u22122 h\u22121contributed the larger fraction of RS, 56%, while the heterotrophic component flux of 0.15 g CO2 m\u22122 h\u22121accounted for the remaining 44% (Table 3). As for the three end-member model, there was little evidence of large differences in these figures between morning and afternoon.", "measurement_extractions": [ { "quantity": "0.18 g CO2 m\u22122 h\u22121", "unit": "g CO2 m\u22122 h\u22121", "measured_entity": "autotrophic flux", "measured_property": null }, { "quantity": "56%", "unit": "%", "measured_entity": "RS", "measured_property": "autotrophic flux of 0.18 g CO2 m\u22122 h\u22121" }, { "quantity": "0.15 g CO2 m\u22122 h\u22121", "unit": "g CO2 m\u22122 h\u22121", "measured_entity": "heterotrophic component flux", "measured_property": null }, { "quantity": "44%", "unit": "%", "measured_entity": "RS", "measured_property": "heterotrophic component flux of 0.15 g CO2 m\u22122 h\u22121" } ], "split": "test", "docId": "S0038071711004354-2389", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Undoubtedly, the possibility of having the CO2 production constrained within the top 30 cm of the soil profile, as well as estimating \u03b413CRs on a highly porous sandy soil, represented a fundamental advantage for the application of the three end-member model in this study. In a porous soil, the gradients in CO2 concentration and 13CO2 abundance adjust very quickly across depth. Thus, the soil CO2 and \u03b413C profiles reflect the depth-integrated CO2 sources (biotic processes) rather than the local fluctuations in production rates due to transient changes in CO2 diffusivity (abiotic processes) (Maseyk et al., 2009). Nevertheless, the results of this study demonstrate the importance of partitioning soil efflux considering both the 13C discrimination of respiratory components and their mass-specific rate of CO2 production.", "measurement_extractions": [ { "quantity": "within the top 30 cm", "unit": "cm", "measured_entity": "soil profile", "measured_property": "CO2 production" } ], "split": "test", "docId": "S0038071711004354-2578", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Time series of the dual-chamber test using a grassland mesocosm maintained under glasshouse conditions. (a) The \u03b413C\u2013CO2 in the bottom-chamber. The initial \u03b413C (approximately \u221214\u2030) represents the \u03b413C of ambient CO2 inside the glasshouse. (b) CO2 concentration measured in the bottom (Cb), in the top (Ct), and reference chamber (Cr). (c) Soil surface flux measured by the dual-chamber (RS), and CO2 diffusion coefficient across the dual-chamber (Dc). Data are shown from the time of deployment of the dual-chamber on the soil surface to the time when the \u03b413C value of the CO2 stabilised after approximately 2.5 h at \u221228.5 \u00b1 0.33\u2030 (mean \u00b1 SD, n = 60).", "measurement_extractions": [ { "quantity": "60", "unit": null, "measured_entity": "dual-chamber test", "measured_property": "n" }, { "quantity": "approximately \u221214\u2030", "unit": "\u2030", "measured_entity": "ambient CO2", "measured_property": "\u03b413C" }, { "quantity": "approximately 2.5 h", "unit": "h", "measured_entity": "\u03b413C value of the CO2", "measured_property": "stabilised" }, { "quantity": "\u221228.5 \u00b1 0.33\u2030", "unit": "\u2030", "measured_entity": "CO2", "measured_property": "\u03b413C" } ], "split": "test", "docId": "S0038071711004354-755", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In previous work (Binkley and Harman, 2005), we introduced the study of dependence clusters in terms of program slicing and demonstrated that large dependence clusters were (perhaps surprisingly) common, both in production (closed source) code and in open source code (Harman et al., 2009). Our findings over a large corpus of C code was that 89% of the programs studied contained at least one dependence cluster composed of 10% or more of the program's statements. The average size of the programs studied was 20KLoC, so these clusters of more than 10% denoted significant portions of code. We also found evidence of super-large clusters: 40% of the programs had a dependence cluster that consumed over half of the program.", "measurement_extractions": [ { "quantity": "89%", "unit": "%", "measured_entity": "programs studied", "measured_property": "contained at least one dependence cluster composed of 10% or more of the program's statements" }, { "quantity": "one", "unit": null, "measured_entity": "dependence cluster", "measured_property": null }, { "quantity": "10%", "unit": "%", "measured_entity": "program's statements", "measured_property": "at least one dependence cluster" }, { "quantity": "more than 10%", "unit": "%", "measured_entity": "program's statements", "measured_property": "clusters" }, { "quantity": "20KLoC", "unit": "KLoC", "measured_entity": "programs studied", "measured_property": "average size" }, { "quantity": "40%", "unit": "%", "measured_entity": "programs", "measured_property": "dependence cluster that consumed over half of the program" }, { "quantity": "over half", "unit": null, "measured_entity": "program", "measured_property": "dependence cluster" } ], "split": "test", "docId": "S016412121300188X-3207", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 5 shows the average slice size deviation when using the lower two settings compared to the highest. On average, the Low setting produces slices that are 14% larger than the High setting. Program userv has the largest deviation of 37% when using the Low setting. For example, in userv the minimal pointer analysis fails to recognize that the function pointer oip can never point to functions sighandler_alrm and sighandler_child and includes them as called functions at call sites using *oip, increasing slice size significantly. In all 30 programs, the Low setting yields larger slices compared to the High setting.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "settings", "measured_property": null }, { "quantity": "14%", "unit": "%", "measured_entity": "Low setting produces slices", "measured_property": "larger than the High setting" }, { "quantity": "37%", "unit": "%", "measured_entity": "Program userv", "measured_property": "largest deviation" }, { "quantity": "30", "unit": null, "measured_entity": "programs", "measured_property": null } ], "split": "test", "docId": "S016412121300188X-4207", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 2 shows a steady quadrupole flow at depths 100, 500, 1000, and 1500 m with A(z) and B(z) defined as in (28). Notice that the quadrupole strength varies linearly with depth.", "measurement_extractions": [ { "quantity": "100, 500, 1000, and 1500 m", "unit": "m", "measured_entity": "steady quadrupole flow", "measured_property": "depths" } ], "split": "test", "docId": "S0167278913001450-12425", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Electrospinning: The polymer solution was transferred to a 1 ml or 5 ml syringe and positioned in the pump for the electrospinning process. The solution was pushed towards the electrospinning nozzle at a rate of 1 ml/h and the voltage was set at 12.2 kV. The electrospun fibres were collected in the form of a random mesh, as described previously in [7].", "measurement_extractions": [ { "quantity": "1 ml or 5 ml", "unit": "ml", "measured_entity": "syringe", "measured_property": null }, { "quantity": "1 ml/h", "unit": "ml/h", "measured_entity": "solution was pushed towards the electrospinning nozzle", "measured_property": "rate" }, { "quantity": "12.2 kV", "unit": "kV", "measured_entity": "electrospinning nozzle", "measured_property": "voltage" } ], "split": "test", "docId": "S0167577X13006393-662", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The XPS analysis also reveals low calcium phosphate levels in the PLGA\u2013HA samples, despite the fact that the fibres are filled with a large amount of nanoparticles. Since XPS is a surface chemical analysis technique (with analysis depth less than 10 nm), this suggests that the nanoparticles lying at the surface of the fibres were embedded in a thin layer of polymer. The presence of carbon observed by XPS at the surface of the HA nanoparticles might result from atmospheric contamination by CO2 and volatile fatty acids. At the surface of the CaP fibres, an additional reason for the presence of carbon might be the existence of residual atoms from the degradation\u2013evaporation of the PLGA polymer. This would explain the higher proportion of carbon when compared to the native HA nanopowder and why the ratio of oxygen to carbon is lower. The slightly lower percentage of oxygen in CaP fibres, compared to the HA nanopowder, may result from the loss of OH groups (dehydroxylation) that occur at high temperature. For pure HA, this reaction is known to occur at sintering temperatures below 1200 \u00b0C with a conversion degree of 0.4 to 0.5 [25]. A simple way to present the reaction formula for dehydroxylation would beCa10(PO4)6(OH)2\u2192Ca10(PO4)6O+H2O", "measurement_extractions": [ { "quantity": "less than 10 nm", "unit": "nm", "measured_entity": "XPS", "measured_property": "analysis depth" }, { "quantity": "below 1200 \u00b0C", "unit": "\u00b0C", "measured_entity": "dehydroxylation", "measured_property": "sintering temperatures" }, { "quantity": "0.4 to 0.5", "unit": null, "measured_entity": "dehydroxylation", "measured_property": "conversion degree" } ], "split": "test", "docId": "S0167577X13006393-801", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "If the sonic anemometer is to be used to evaluate the accuracy of the lidar-measured wind speeds, confidence in the accuracy of the wind speeds measured by this instrument must be high. In a previous study Barlow et al. (2011b) carried out wind tunnel simulations of the flow around BT Tower and the lattice tower on which the anemometer is located. During these simulations it was observed that both the tower and the lattice distorted the flow at the position of a sonic anemometer installed on top of the lattice, and correction factors were developed for this position. These correction factors cannot be directly applied to the data presented here, as the sonic anemometer has been moved since the original installation. Although the corrections applied by Barlow et al. (2011b) were small (\u22482% of the mean wind speed), data from the sonic anemometer are examined here to determine whether flow distortion has a substantial effect on the instrument in this new position. Since the new position is higher, and therefore further from the lattice tower, it is expected that any error will be smaller.", "measurement_extractions": [ { "quantity": "\u22482%", "unit": "%", "measured_entity": "mean wind speed", "measured_property": "corrections" } ], "split": "test", "docId": "S0167610513001001-1566", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 5 shows \u03b8=tan\u22121(w/U) against wind speed in gate closest to the height of the BT Tower (midpoint=180 m). Below the median wind speed of 7.83 m s\u22121 the variability in \u03b8 increases dramatically. Low wind speeds are often associated with unstable conditions where larger fluctuations in w would be more common, increasing the variability in \u03b8. In fitting the sine function only data where the horizontal wind speed was greater than the median wind speed were used, in order to reduce the variability in \u03b8.", "measurement_extractions": [ { "quantity": "180 m", "unit": "m", "measured_entity": "height of the BT Tower", "measured_property": "midpoint" }, { "quantity": "7.83 m s\u22121", "unit": "m s\u22121", "measured_entity": "wind", "measured_property": "speed" } ], "split": "test", "docId": "S0167610513001001-1618", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "After removing data from the sonic anemometer affected by flow distortion, and correcting for a \u22480.5\u00b0 tilt of the lidar, the data from the two instruments were compared. The horizontal wind speeds were averaged over 60 min to reduce variability. A strong correlation was found between the two datasets, although the lidar has a tendency to overestimate the wind speed by \u22480\u20130.5 m s\u22121 at speeds of less than 20 m s\u22121. At higher wind speeds there are few data, so it is not possible to draw a robust conclusion for these conditions. The error in the lidar-derived wind speeds varies with the stability of the atmosphere; from 0.36 m s\u22121 in stable conditions, to 1.05 m s\u22121 in unstable conditions, and 0.86 m s\u22121 in neutral conditions.", "measurement_extractions": [ { "quantity": "\u22480.5\u00b0", "unit": "\u00b0", "measured_entity": "lidar", "measured_property": "tilt" }, { "quantity": "two", "unit": null, "measured_entity": "instruments", "measured_property": null }, { "quantity": "over 60 min", "unit": "min", "measured_entity": "horizontal wind speeds", "measured_property": "averaged" }, { "quantity": "two", "unit": null, "measured_entity": "datasets", "measured_property": null }, { "quantity": "\u22480\u20130.5 m s\u22121", "unit": "m s\u22121", "measured_entity": "lidar", "measured_property": "overestimate the wind speed" }, { "quantity": "less than 20 m s\u22121", "unit": "m s\u22121", "measured_entity": "wind", "measured_property": "speed" }, { "quantity": "0.36 m s\u22121", "unit": "m s\u22121", "measured_entity": "lidar-derived wind speeds", "measured_property": "error" }, { "quantity": "1.05 m s\u22121", "unit": "m s\u22121", "measured_entity": "lidar-derived wind speeds", "measured_property": "error" }, { "quantity": "0.86 m s\u22121", "unit": "m s\u22121", "measured_entity": "lidar-derived wind speeds", "measured_property": "error" } ], "split": "test", "docId": "S0167610513001001-1769", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Mean, median and interquartile range of lidar error variances due to sampling rate, for averaging periods of 60 min.", "measurement_extractions": [ { "quantity": "60 min", "unit": "min", "measured_entity": "Mean, median and interquartile range of lidar error variances due to sampling rate", "measured_property": "averaging periods" } ], "split": "test", "docId": "S0167610513001001-858", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A huge advantage of Scenarios 2. * is that they do not run into any crucial SLA violation (except for Scenario 2.3), but achieve a higher utilization as compared to Scenario 3. As to the reallocation actions, of course, Scenario 1 and 3 do not execute any, but also for the autonomic management in Scenarios 2. *, the amount of executed reallocation actions for most scenarios stays below 10%. Only Scenario 2.7 executes actions in 19.8% of the cases on average of the time. Five out of eight scenarios stay below 5% on average.", "measurement_extractions": [ { "quantity": "below 10%", "unit": "%", "measured_entity": "reallocation actions", "measured_property": "executed" }, { "quantity": "19.8%", "unit": "%", "measured_entity": "cases", "measured_property": "executes actions" }, { "quantity": "below 5%", "unit": "%", "measured_entity": "Five out of eight scenarios", "measured_property": "executes actions" }, { "quantity": "Five out of eight", "unit": null, "measured_entity": "scenarios", "measured_property": "stay below 5% on average" } ], "split": "test", "docId": "S0167739X12001525-6016", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In Nicaragua no significant differences in changes of SOC stock between the organic and conventional treatments were detected for any soil depth. Nevertheless, the trends were generally similar to Costa Rica, with a greater increase of SOC stock at 0\u201310 cm depth in the organic compared with the conventional treatment and in the 20\u201340 cm depth a similar decrease in SOC stock between them (Fig. 4). In Nicaragua, like Costa Rica, there was a positive correlation between the mass of organic fertiliser inputs and changes in 0\u201310 cm depth SOC (r2 = 0.07, p < 0.05).", "measurement_extractions": [ { "quantity": "0\u201310 cm", "unit": "cm", "measured_entity": "SOC stock", "measured_property": "depth" }, { "quantity": "20\u201340 cm", "unit": "cm", "measured_entity": "SOC stock", "measured_property": "depth" }, { "quantity": "0\u201310 cm", "unit": "cm", "measured_entity": "SOC", "measured_property": "depth" }, { "quantity": "0.07", "unit": null, "measured_entity": "positive correlation", "measured_property": "r2" }, { "quantity": "< 0.05", "unit": null, "measured_entity": "positive correlation", "measured_property": "p" } ], "split": "test", "docId": "S0167880913001229-1323", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Increasing sink strength can lead to higher biomass yield. PHOTOPERIOD RESPONSE 1 is a gene in potato that has been associated with the accumulation of starch in the tubers. Over-expression of the endogenous ortholog of this gene in P. trichocarpa resulted in starch accumulation in stem and roots, and, consequently, significantly more stem and root biomass [110]. Many of the transgenic plants had malformed root systems that may have contributed to a 50% mortality rate in the greenhouse. Subsequently, the survivors were able to develop normal root systems. Careful selection for growth and vigor in the greenhouse phase and use of bare-root seedlings for transplanting may help ensure that only plants with satisfactory root systems are planted out in the field.", "measurement_extractions": [ { "quantity": "50%", "unit": "%", "measured_entity": "transgenic plants", "measured_property": "mortality rate" } ], "split": "test", "docId": "S0168945213001805-4574", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "On the necessity of field trialsFast growth in the GMO greenhouse does not always translate into superior growth in the field. Even with non-transgenic trees, breeders experience difficulty in predicting the mature tree phenotype from greenhouse stock (so called \u2018age-age\u2019 correlations). Multiple genetic factors, assembled by breeding into one genotype to contribute to a specific phenotype, may respond differently at different growth stages to various aspects of the field environment. At the earliest, the volume of 10-year-old Populus trees can be reasonably predicted only from the volumes of three-year-old saplings [221]. Other slower-growing species require data from much older (>10-year-old) trees to predict potential harvest indices at maturity. In more than two decades of transgenesis, the single-gene/single-trait approach of molecular biologists has successfully generated genetically modified organisms with relatively stable trait expression [222]. Transgene instability, while common during the early phases of selection and growth in vitro, tends to be relatively rare under field conditions [2]. Transgenic plants modified with single potent genes to produce novel traits like GUS [223], herbicide resistance [224], and even complex characters like superior growth [71], have all been shown to stably display the desired phenotypes in the field. The expression of the rolC transgene was stable in 19-year-old tissue cultures and 18-year-old glasshouse-grown trees [225]. Except for viral approaches like VIGS/VOX [219], reversion to wild type has been relatively rare and this is often detected quite early primarily because over-expression of key genes has such a remarkable effect on the transgenic phenotype. Nevertheless, it is always prudent to implement field trials of appropriate duration, especially when transgenics are involved. Transgenic poplar (P. alba) that overproduced XYLOGLUCANASE grew 40% taller than wild type in growth chambers but subsequently grew poorly in field trials [109]. In another example, incomplete barstar attenuation of the cytotoxicity of a poplar LEAFY promoter::barnase construct in transgenic hybrid poplar (P. tremula \u00d7 alba) led to substantially reduced growth rates in the field, prompting the authors to highlight the importance of field testing to identify pleiotropic effects [127].Another justification for field testing is that genetic transformation methods that require regeneration from callus are prone to random somaclonal mutations during in vitro culture [226]. Unfortunately, the effects of somaclonal mutations may or may not be related to the target phenotype, and their effects on the phenotype may be stage- or tissue specific. Hence, even transgenics that express stable and potent genes, such as those that produce insecticidal proteins, need to be tested in field trials at multiple locations and for specific durations. Such comprehensive and long-term testing may expose those transgenic plants that have otherwise cryptic traits due to the mutation of non-target genes. In addition, lab assays need to be designed to be more predictive of performance in the field. For example, leaf-feeding assays in the lab indicated that hybrid aspen (P. tremula \u00d7 alba) over-expressing a gene for POLYPHENOL OXIDASE produced leaves that were toxic to forest tent caterpillar (Malacosoma disstria) [161] but this could not be replicated in a subsequent field trial [162]. Again, this result emphasizes the need to verify gene activity at various stages of the plant's cycle, from in-vitro cultivation to growth in the field. The genes featured in Fig. 1 have been tested and proven to generate the expected phenotypes in various independent experiments involving unrelated plant taxa. However, the effects of their simultaneous introduction into a plant genome are difficult to predict. The best gene combinations for biomass yield will only be revealed by the transgenic line(s) with the best growth under various test conditions. Hence, comprehensive and rigorous field testing, preferably in multiple sites, is imperative. In multi-trait engineering, it may be wise to reduce the stringency of selection prior to field planting and test as many of the transgenics as clones in experiments replicated in multiple plots over several locations and then monitored through time. Such field trials should be managed in accordance with standard commercial practices for each location. This intermediate stage may reveal, among others, the suitability of the transgenic plant for clonal propagation and allow the generation of sufficient planting material for multi-location testing. Serial selection of the most promising transgenic plants can be performed at various times thereafter. All trials need to be monitored for as long as practicable\u2014in the face of current regulations that require prompt elimination of test materials just before they start flowering.", "measurement_extractions": [ { "quantity": "10-year", "unit": "year", "measured_entity": "Populus trees", "measured_property": "old" }, { "quantity": "three-year", "unit": "year", "measured_entity": "saplings", "measured_property": "old" }, { "quantity": ">10-year", "unit": "year", "measured_entity": "trees", "measured_property": "old" }, { "quantity": "two", "unit": null, "measured_entity": "decades of transgenesis", "measured_property": null }, { "quantity": "19-year", "unit": "year", "measured_entity": "tissue cultures", "measured_property": "old" }, { "quantity": "18-year", "unit": "year", "measured_entity": "glasshouse-grown trees", "measured_property": "old" }, { "quantity": "40%", "unit": "%", "measured_entity": "Transgenic poplar (P. alba) that overproduced XYLOGLUCANASE", "measured_property": "grew" } ], "split": "test", "docId": "S0168945213001805-5396", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Schematic fabrication process for the dual-layer carbon film. The processes were carried out in a silica tube furnace at 1000 \u00b0C. The surface of SiC was converted into CDC by etching in a chlorine-containing gas mixture. The shape of the sample stayed unchanged. The CVD layer was prepared by pyrolyzing CCl4 on top surface of CDC layer and increased the sample thickness.", "measurement_extractions": [ { "quantity": "1000 \u00b0C", "unit": "\u00b0C", "measured_entity": "silica tube furnace", "measured_property": null } ], "split": "test", "docId": "S0257897213007573-555", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "AFM images of [a] as-received SiC wafer [2 \u00d7 2 \u03bcm2], [c] CDC layer [2 \u00d7 2 \u03bcm2], [e] the dual-layer film [20 \u00d7 20 \u03bcm2], and their corresponding 3D profiles [b], [d], and [f]. The surface roughness [RMS] increases from 0.22 nm for SiC to 1.46 nm for CDC, and the roughness of dual-layer film is the largest of 54.68 nm.", "measurement_extractions": [ { "quantity": "2 \u00d7 2 \u03bcm2", "unit": "\u03bcm2", "measured_entity": "SiC wafer", "measured_property": null }, { "quantity": "2 \u00d7 2 \u03bcm2", "unit": "\u03bcm2", "measured_entity": "CDC layer", "measured_property": null }, { "quantity": "20 \u00d7 20 \u03bcm2", "unit": "\u03bcm2", "measured_entity": "dual-layer film", "measured_property": null }, { "quantity": "0.22 nm", "unit": "nm", "measured_entity": "SiC", "measured_property": "surface roughness [RMS]" }, { "quantity": "1.46 nm", "unit": "nm", "measured_entity": "CDC", "measured_property": "surface roughness [RMS]" }, { "quantity": "54.68 nm", "unit": "nm", "measured_entity": "dual-layer film", "measured_property": "roughness" } ], "split": "test", "docId": "S0257897213007573-574", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The Raman features of the CVD layer are similar to that of the CDC layer, although the position of D-band shifts to the higher wavenumber at ~ 1354 cm\u2212 1. Since the CVD carbon nucleates and grows on the top of CDC layer, top layer may copy the structure of CDC layer in some extent and therefore their Raman feature is similar. The up-shifted D-band of CVD layer may be attributed to the increased size of the graphite microcrystalline. It is also necessary to note that the densification of the CVD layer and the reduced internal stress may lead to the difference in Raman spectrum. The Raman spectrum of the CVD layer was also fitted with 2 + 2 Gaussian mode. The integrated intensity ratio (ID/IG) reduces to 1.32, corresponding to an increased La of 3.75 nm compared to CDC layer.", "measurement_extractions": [ { "quantity": "~ 1354 cm\u2212 1", "unit": "cm\u2212 1", "measured_entity": "D-band", "measured_property": "position" }, { "quantity": "3.75 nm", "unit": "nm", "measured_entity": "CVD layer", "measured_property": "La" }, { "quantity": "1.32", "unit": null, "measured_entity": "ID/IG", "measured_property": "ratio" } ], "split": "test", "docId": "S0257897213007573-899", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 4 shows the AFM images and corresponding 3D profiles of SiC wafer, CDC layer and dual-layer film, indicating surface morphology evolution in the process. The as-received SiC surface in Fig. 4a and b is characterized by highly uniform and flat terraces. The surface roughness RMS (root mean square roughness) is evaluated as 0.22 nm. The step direction and terrace width are determined by the incidental disorientation of the substrate surface with respect to the crystallographic (0001) plane [26]. The step height is smaller than that of the dimension of the 6H-SiC unit cell in the direction perpendicular to the surface (c axis), which may resulted from the silicon oxide layer. The morphology of the CDC layer prepared by chlorination is shown in Fig. 4c and d. The SiC surface undergoes significant modification: it is now covered with numerous nanopores up to 100 nm in width, and the original are disappeared. The formation of CDC is accompanied by substantial changes in the morphology, leading to a considerable increasing surface roughness of 1.46 nm. As a consequence of chlorination, the CDC layer possesses a nanoporous structure and an increasing specific surface area on the surface [6,27]. The nanopores can easily capture the carbon species from the gas phase for the subsequent CVD process, which benefits the nucleation and growth of the CVD layer. After the CVD process, the surface morphology is characterized by spherical or ellipsoidal particles, as shown in Fig. 4e and f. The size of carbon particles is up to approximately 3 \u03bcm in diameter, and the surface roughness is substantially increased to 54.68 nm in RMS.", "measurement_extractions": [ { "quantity": "0.22 nm", "unit": "nm", "measured_entity": "SiC surface", "measured_property": "surface roughness RMS (root mean square roughness)" }, { "quantity": "100 nm", "unit": "nm", "measured_entity": "nanopores", "measured_property": "width" }, { "quantity": "1.46 nm", "unit": "nm", "measured_entity": "CDC layer", "measured_property": "surface roughness" }, { "quantity": "54.68 nm", "unit": "nm", "measured_entity": "CVD layer", "measured_property": "surface roughness" }, { "quantity": "up to approximately 3 \u03bcm", "unit": "\u03bcm", "measured_entity": "carbon particles", "measured_property": "diameter" } ], "split": "test", "docId": "S0257897213007573-959", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Metal\u2013organic framework (MOF) materials show promise for H2 storage and it is widely predicted by computational modelling that MOFs incorporating ultra-micropores are optimal for H2 binding due to enhanced overlapping potentials. We report the investigation using inelastic neutron scattering of the interaction of H2 in an ultra-microporous MOF material showing low H2 uptake capacity. The study has revealed that adsorbed H2 at 5 K has a liquid recoil motion along the channel with very little interaction with the MOF host, consistent with the observed low uptake. The low H2 uptake is not due to incomplete activation or decomposition as the desolvated MOF shows CO2 uptake with a measured pore volume close to that of the single crystal pore volume. This study represents a unique example of surprisingly low H2 uptake within a MOF material, and complements the wide range of studies on systems showing higher uptake capacities and binding interactions.", "measurement_extractions": [ { "quantity": "5 K", "unit": "K", "measured_entity": "adsorbed H2", "measured_property": null } ], "split": "test", "docId": "S0301010413004096-646", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Metal\u2013organic framework (MOF) complexes are a sub-class of porous solids which show great promise for gas storage and separation due to their high surface area, low framework density, and tuneable functional pore environment [3]. MOF materials are usually built up from metal ions or clusters bridged by organic linkers to afford 3D extended frameworks with the formation of cavities ranging from microporous to mesoporous region. Several members within this MOF family have achieved impressively high H2 adsorption capacities (albeit at cryogenic temperatures, typically at 77 K) [4] with a record of \u223c16 wt% total uptake capacity observed in NU-100 [5] and MOF-200 [6]. However, these high uptake capacities drop dramatically with increasing temperature, and thus none is a practical material. There is thus particular emphasis on optimising the interactions between MOF hosts and adsorbed H2 molecules, and the identification of specific binding interactions and properties of gases within confined space represents an important methodology for the development of better materials that may lead us to systems of practical use. In situ neutron powder diffraction (NPD) at below 10 K has been used previously to determine the locations of D2 within a few best-behaving MOF materials incorporating exposed metal sites [7\u201312]. It has been found that D2 can bind directly to vacant sites on metal centres, and that the adsorbed D2 molecules have molecular separations comparable to that to D2 in the solid state. These studies have provided invaluable structural rationale for their observed high gas adsorption capacities. Research has thus focused understandably on MOFs with high H2 uptake capacities, while materials showing very low H2 uptake and/or incorporate fully coordinated metal centres are often ignored for this study. Therefore, information on binding interactions within those low-uptake MOF systems is entirely lacking, but can still give important complementary data and potential understanding for the subsequent design and optimisation of hydrogen storage materials.", "measurement_extractions": [ { "quantity": "77 K", "unit": "K", "measured_entity": "cryogenic", "measured_property": "temperatures" }, { "quantity": "\u223c16 wt%", "unit": "wt%", "measured_entity": "H2", "measured_property": "total uptake capacity" }, { "quantity": "below 10 K", "unit": "K", "measured_entity": "In situ neutron powder diffraction (NPD)", "measured_property": null } ], "split": "test", "docId": "S0301010413004096-693", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "INS spectra were recorded on the TOSCA spectrometer at the ISIS Neutron Facility at the Rutherford Appleton Laboratory (UK) for energy transfers between \u223c\u22122 and 500 meV. In this region TOSCA has a resolution of \u223c1% \u0394E/E. The sample of desolvated NOTT-300 (\u223c2.5 g) was loaded into a cylindrical vanadium sample container and connected to a gas handling system. The sample was degassed at 10\u22127 mbar and 120 \u00b0C for 1 day to remove any remaining trace guest solvents. The temperature during data collection was controlled using the instrument built-in cryostat and electric heaters (5 \u00b1 0.2 K). The loading of para-H2 (99.5%) was performed volumetrically at 40\u201350 K in order to ensure that H2 was adsorbed into NOTT-300. Subsequently, the temperature was reduced to 5 K in order to perform the scattering measurements with the minimum achievable thermal motion for H2 molecules.", "measurement_extractions": [ { "quantity": "between \u223c\u22122 and 500 meV", "unit": "meV", "measured_entity": "INS spectra", "measured_property": "energy transfers" }, { "quantity": "\u223c1% \u0394E/E", "unit": "% \u0394E/E", "measured_entity": "TOSCA", "measured_property": "resolution" }, { "quantity": "\u223c2.5 g", "unit": "g", "measured_entity": "sample of desolvated NOTT-300", "measured_property": null }, { "quantity": "10\u22127 mbar", "unit": "mbar", "measured_entity": "sample", "measured_property": "degassed" }, { "quantity": "120 \u00b0C", "unit": "\u00b0C", "measured_entity": "sample", "measured_property": "degassed" }, { "quantity": "1 day", "unit": "day", "measured_entity": "sample", "measured_property": "degassed" }, { "quantity": "5 \u00b1 0.2 K", "unit": "K", "measured_entity": "electric heaters", "measured_property": "temperature" }, { "quantity": "99.5%", "unit": "%", "measured_entity": "para-H2", "measured_property": null }, { "quantity": "40\u201350 K", "unit": "K", "measured_entity": "para-H2", "measured_property": "loading" }, { "quantity": "5 K", "unit": "K", "measured_entity": "scattering measurements", "measured_property": "temperature" } ], "split": "test", "docId": "S0301010413004096-767", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Sites were surveyed using 2 \u00d7 2 m temporary quadrats placed along equally spaced line transects. The separation S (in m) between transects and between quadrats on transects was computed by the formula (Harmer and Morgan, 2009): S=100A/n, where A is the site area (ha) and n the number of quadrats (detailed in Table 1). Quadrats on forest track margins were omitted. In total we surveyed 1140 quadrats. Within each quadrat the species, number and height of all regenerating juveniles (defined here as either seedlings with a height \u2a7d50 cm or saplings with a height >50 cm) were noted. The height of saplings was measured with an extensible folding rule. The incidence of leading stems damaged by browsing on trees <2 m tall was noted. No attempt was made to distinguish the different birch, oak and willow spp. The distance to the nearest seed source (defined as a mature tree) was measured in the field for each tree species (all the sampled plots lay within 250 m of a native seed source). Within each quadrat we recorded the percentage of quadrat area beneath the canopy of each vascular plant species (as 2 or more species can overlap, this can result in a total vegetation cover of more than 100%) as well as the percentage cover of decaying woody debris (stumps, fallen logs and brash). Soil samples were taken from each quadrat and the pH was measured electrometrically using a soil\u2013water paste. We were interested in the effect of brash on regeneration density so in sites that had been recently clearfelled (U6a, F2 and F4) a transect with equally spaced quadrats was oriented along a windrow and, parallel to this, another transect along the adjacent area (interrow) between the windrows. It was not possible to do this analysis on sites that had been clearfelled more than a few years ago as the vegetation growth and rotting of the brash made it increasingly difficult to discern windrows.", "measurement_extractions": [ { "quantity": "2 \u00d7 2 m", "unit": "m", "measured_entity": "temporary quadrats", "measured_property": null }, { "quantity": "1140", "unit": null, "measured_entity": "quadrats", "measured_property": null }, { "quantity": "\u2a7d50 cm", "unit": "cm", "measured_entity": "seedlings", "measured_property": "height" }, { "quantity": ">50 cm", "unit": "cm", "measured_entity": "saplings", "measured_property": "height" }, { "quantity": "<2 m", "unit": "m", "measured_entity": "trees", "measured_property": "tall" }, { "quantity": "within 250 m", "unit": "m", "measured_entity": "sampled plots", "measured_property": "native seed source" }, { "quantity": "2 or more", "unit": null, "measured_entity": "species", "measured_property": null }, { "quantity": "more than 100%", "unit": "%", "measured_entity": "vegetation cover", "measured_property": null } ], "split": "test", "docId": "S0378112713005288-1800", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Regeneration density against distance from seed source is plotted in Fig. 2. In general, birch showed a broad shoulder of dense regeneration close to source, followed by a very rapid decline and then a long tail consisting of a slow decline. Linear regression found a logarithmic decline in birch density with increased distance to seed source (see Fig. 2). No significant correlation between distance from seed source (for distances up to 100 m from the source) and regeneration density was seen for animal-dispersed species (oak and rowan). However, the regeneration of both rowan and oak were still strongly clumped (R = 0.23 and 0.28 respectively, both p < 0.0001).", "measurement_extractions": [ { "quantity": "up to 100 m", "unit": "m", "measured_entity": "animal-dispersed species", "measured_property": "distances" }, { "quantity": "0.23", "unit": null, "measured_entity": "rowan", "measured_property": "R" }, { "quantity": "0.28", "unit": null, "measured_entity": "oak", "measured_property": "R" }, { "quantity": "< 0.0001", "unit": null, "measured_entity": "p", "measured_property": null } ], "split": "test", "docId": "S0378112713005288-1916", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "a. Profiles of TKE corresponding to selected magnitudes of depth-averaged velocity during uprush (+), during backwash before time of maximum backwash velocity (\u25cf) and during late backwash (x). Horizontal lines indicate corresponding measured water depths during uprush (-), during backwash before time of maximum backwash velocity (--) and during late backwash (-.-.). Results are shown for the 1.3 mm sand-rough beach at x = 0.072 m (top panel), x = 0.772 m (middle) and x = 1.567 m (bottom). b. TKE profiles for the 8.4 mm gravel-rough beach. See caption Fig. 15a for details.", "measurement_extractions": [ { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "sand-rough beach", "measured_property": null }, { "quantity": "0.072 m", "unit": "m", "measured_entity": "1.3 mm sand-rough beach", "measured_property": null }, { "quantity": "0.772 m", "unit": "m", "measured_entity": "1.3 mm sand-rough beach", "measured_property": null }, { "quantity": "1.567 m", "unit": "m", "measured_entity": "1.3 mm sand-rough beach", "measured_property": null }, { "quantity": "8.4 mm", "unit": "mm", "measured_entity": "gravel-rough beach", "measured_property": null } ], "split": "test", "docId": "S0378383911001669-1203", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 18 presents typical bed shear stress time-series estimated from the velocity measurements via the momentum balance, log-law and Reynolds stress methods. The example results shown are for x = 0.772 m on the 1.3 mm sand-rough and 5.4 mm gravel-rough beaches. The results from the 1.3 mm sand-rough beach show a good overall agreement between the momentum balance and the log law method. The agreement is poorer close to flow reversal, when the momentum balance method is least accurate because all balance terms are very small. The Reynolds stress method agrees with the other two methods in the uprush, but in the backwash it produces results that lag behind them by up to 1 s. This delay is likely due to the same reason as that causing the delay in near-bed turbulent shear stress relative to the near-bed velocity, since the near-bed velocity is closely related to the bed shear stress. For the gravel-rough beaches the accuracy of the measurements was not sufficient to obtain useful estimates from the momentum balance method (lower panel of Fig. 18). The limitation of the control volume size relative to the size of the sediments appears to have negatively affected the results. Therefore, even though the momentum balance method is better-founded theoretically than the log-law method, in the following we concentrate on bed shear stress results obtained using the log-law method. Another benefit of the log-law method is that it has been extensively used in the literature, making comparison of our results with previous results more straightforward.", "measurement_extractions": [ { "quantity": "0.772 m", "unit": "m", "measured_entity": "1.3 mm sand-rough and 5.4 mm gravel-rough beaches", "measured_property": null }, { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "beaches", "measured_property": null }, { "quantity": "5.4 mm", "unit": "mm", "measured_entity": "gravel-rough beaches", "measured_property": null }, { "quantity": "1.3 mm", "unit": "mm", "measured_entity": "sand-rough beach", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "methods", "measured_property": null }, { "quantity": "up to 1 s", "unit": "s", "measured_entity": "backwash", "measured_property": "lag behind" } ], "split": "test", "docId": "S0378383911001669-2205", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Time-series of measured (symbols) and model-predicted (solid line) shoreline position for the 1.5 mm and 8.5 mm beaches.", "measurement_extractions": [ { "quantity": "1.5 mm and 8.5 mm", "unit": "mm", "measured_entity": "beaches", "measured_property": null } ], "split": "test", "docId": "S0378383912000130-1041", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Profiles of measured surface flow (grey line) and pressure transducer measurements (symbol \u25b3) and model-predicted surface and groundwater profiles (black solid and black dashed lines respectively) for the 8.5 mm beach at several selected times.", "measurement_extractions": [ { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "test", "docId": "S0378383912000130-1090", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In the case of the 8.5 mm beach the numerical predictions over-estimate the groundwater level, particularly in the backwash. This is probably due to the relatively simple parameterisation of the groundwater flow.", "measurement_extractions": [ { "quantity": "8.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null } ], "split": "test", "docId": "S0378383912000130-3726", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For the 1.5 mm beach the agreement between model predictions and measurements is quite good after t = 2 s. At the early stages of swash, however, there is a disagreement: the experimental results show an approximately constant gradient whereas the simulation results suggest a steep increase upon the bore arrival, followed by a gradual decline. At the initial stages of infiltration, when the height of surface water is much greater than the penetration depth, hydraulic gradients are expected to be higher than later on. Furthermore, when the penetration depth is small the experimental error in evaluating gradients is large. For these reasons the discrepancy between the simulation and the experimental results is probably due to experimental error.", "measurement_extractions": [ { "quantity": "1.5 mm", "unit": "mm", "measured_entity": "beach", "measured_property": null }, { "quantity": "2 s", "unit": "s", "measured_entity": "agreement between model predictions and measurements", "measured_property": "t" } ], "split": "test", "docId": "S0378383912000130-3739", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The numerical predictions of surface and subsurface flow behaviour for a bore-driven swash on two permeable coarse-grained beaches (1.5 mm and 8.5 mm sediment) are in good agreement with large-scale laboratory swash measurements. The numerical results capture the main swash flow features and wetting front profiles in uprush and backwash, and give good predictions of hydraulic gradient and infiltrated volume time-series across the swash zone for both beaches. The numerical predictions of the pore-air pressure build-up within the two sediments are in good agreement with the large-scale laboratory measurements. The time when pressure first changes (i.e. the arrival of the pressure front) is well-predicted across the swash zone, as are the magnitudes of pressure head during uprush and backwash. The numerical model captures well the groundwater response to surface\u2013subsurface water exchange, with reasonable agreement between the model-predicted and measured exit point during the backwash. Discrepancies between model and experimental results are primarily due to the relatively simple parametrisation of the bed shear stress for the surface flow and the approximation of the subsurface flow as (coupled) one-dimensional processes.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "permeable coarse-grained beaches", "measured_property": null }, { "quantity": "1.5 mm and 8.5 mm", "unit": "mm", "measured_entity": "two permeable coarse-grained beaches", "measured_property": "sediment" }, { "quantity": "two", "unit": null, "measured_entity": "sediments", "measured_property": null } ], "split": "test", "docId": "S0378383912000130-3891", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The channel is 40 m wide and 90 m long with two symmetrical wall constrictions from both sides with angle \u03b2 = 5\u00b0 to the x-direction, starting from x = 10 m. The simulations were run on two different grids: a 121 \u00d7 53 regular curvilinear grid, with nearly square-shaped calculation cells and a highly distorted grid obtained by deforming the above-mentioned 121 \u00d7 53 regular grid. The initial and inflow conditions are the water depth h = 1 m and the Froude number Fr = 2.5.", "measurement_extractions": [ { "quantity": "40 m", "unit": "m", "measured_entity": "channel", "measured_property": "wide" }, { "quantity": "90 m", "unit": "m", "measured_entity": "channel", "measured_property": "long" }, { "quantity": "two", "unit": null, "measured_entity": "symmetrical wall constrictions", "measured_property": null }, { "quantity": "5\u00b0", "unit": "\u00b0", "measured_entity": "symmetrical wall constrictions from both sides", "measured_property": "angle \u03b2" }, { "quantity": "10 m.", "unit": "m", "measured_entity": "x", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "different grids", "measured_property": null }, { "quantity": "121 \u00d7 53", "unit": null, "measured_entity": "regular curvilinear grid", "measured_property": null }, { "quantity": "121 \u00d7 53", "unit": null, "measured_entity": "regular grid", "measured_property": null }, { "quantity": "1 m", "unit": "m", "measured_entity": "initial and inflow conditions", "measured_property": "water depth" }, { "quantity": "2.5", "unit": null, "measured_entity": "initial and inflow conditions", "measured_property": "Froude number" } ], "split": "test", "docId": "S0378383913001567-6892", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In order to test convergence with time discretisation a sequence of time steps is considered. As in Shi et al. (2001) the sequence is given by \u2206t/i with \u2206t = 0.2 m and i = 1,2,\u2026,10. Spatial discretisation is constant at \u2206x = 0.2 m for all the tests. Fig. 16 shows convergence rates with time refinement. The average Rij obtained is 2.33. This value obtained with the proposed third-order Runge\u2013Kutta scheme is similar to that obtained by Shi et al.(2003) with a fourth-order Adams\u2013Bashforth\u2013Moulton predictor\u2013corrector scheme. It has to be noted that unlike the latter scheme, the proposed Runge\u2013Kutta scheme allows an adaptive time-stepping as suggested by Shi et al. (2012).", "measurement_extractions": [ { "quantity": "0.2 m", "unit": "m", "measured_entity": "sequence", "measured_property": "\u2206t" }, { "quantity": "1,2,\u2026,10.", "unit": null, "measured_entity": "sequence", "measured_property": "i" }, { "quantity": "0.2 m", "unit": "m", "measured_entity": "tests", "measured_property": "\u2206x" }, { "quantity": "2.33", "unit": null, "measured_entity": "average Rij", "measured_property": null } ], "split": "test", "docId": "S0378383913001567-7073", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Striga hermonthica (Del.) Benth. is a persistent threat to pearl millet [Cenchrus americanus (L.) Morrone, comb. nov.] production, especially in West Africa. This study aimed at evaluating the response of a diversified pearl millet genepool to five cycles of recurrent selection targeting Striga resistance and panicle yield, and to a lesser extent downy mildew [Sclerospora graminicola (Sacc.) J. Schroet.] resistance. Two-hundred full-sib families (FS) representing the C5 selection cycle were evaluated together with the genepool parental landraces, experimental varieties derived from previous cycles and local checks in Striga-infested fields at Sador\u00e9 (Niger) and Cinzana (Mali). Substantial and mostly significant selection progress could be documented. The accumulated percentage gain from selection amounted to 51%/1% lower Striga infestation (measured by area under Striga number progress curve, ASNPC), 46%/62% lower downy mildew incidence, and 49%/31% higher panicle yield of the C5-FS compared to the mean of the genepool parents at Sador\u00e9/Cinzana, respectively. Experimental varieties selected from previous cycles also revealed lower ASNPC and mostly higher yield compared to genepool parents at their selection sites. Significant genetic variation among the C5-FS and operative heritabilities of 76% (Cinzana), 84% (Sador\u00e9) and 34% (combined across locations) for ASNPC will enable continued selection gain for Striga resistance. High genotype \u00d7 environment interaction variances for all target traits suggest that different experimental varieties need to be extracted from the genepool for different sites. The genepool-derived varieties will be further validated on-farm and are expected to contribute to integrated Striga control in pearl millet in West Africa.", "measurement_extractions": [ { "quantity": "five", "unit": null, "measured_entity": "cycles", "measured_property": null }, { "quantity": "Two-hundred", "unit": null, "measured_entity": "full-sib families", "measured_property": null }, { "quantity": "51%", "unit": "%", "measured_entity": "Striga-infested fields at Sador\u00e9 (Niger)", "measured_property": "lower Striga infestation" }, { "quantity": "1%", "unit": "%", "measured_entity": "Striga-infested fields", "measured_property": "lower Striga infestation" }, { "quantity": "46%/62%", "unit": "%", "measured_entity": "accumulated percentage gain from selection", "measured_property": "lower downy mildew incidence" }, { "quantity": "49%/31%", "unit": "%", "measured_entity": "accumulated percentage gain from selection", "measured_property": "higher panicle yield of the C5-FS" }, { "quantity": "76%", "unit": "%", "measured_entity": "operative heritabilities", "measured_property": null }, { "quantity": "84%", "unit": "%", "measured_entity": "operative heritabilities", "measured_property": null }, { "quantity": "34%", "unit": "%", "measured_entity": "operative heritabilities", "measured_property": null } ], "split": "test", "docId": "S037842901300244X-1427", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Data were collected at both sites on a plot basis for traits related to pearl millet phenological and agronomical characters, downy mildew (DM) incidence, and Striga resistance. The flowering time (FLO) in days after sowing (das) was recorded when 50% of the plants had exerted stigmas. During harvest, the number of productive panicles was counted and weighed to determine the panicle yield (HYD) (g m\u22122). The number of hills per plot with downy mildew was recorded at tillering, verified/adjusted after flowering and used for calculating the downy mildew (DM) incidence (in percentage): number of infested hills, divided by the total number of hills after emergence, multiplied by 100. In addition, the number of emerged Striga plants for each plot was recorded at 67, 101 and 121 das. Successive Striga counts were then used to calculate the \u201cArea under Striga Number Progress Curve\u201d (ASNPC) (Haussmann et al., 2000):ASNPC=\u2211i=0n\u22121Yi+Y(i+1)2(t(i+1)\u2212ti)where n is the number of Striga assessment dates, Yi the Striga count at the ith assessment date, ti the days after sowing at the ith assessment date, t0 is 0, and Y0 is 0. Low ASNPC means values indicate resistance, and high values susceptibility to Striga.", "measurement_extractions": [ { "quantity": "50%", "unit": "%", "measured_entity": "plants", "measured_property": "exerted stigmas" }, { "quantity": "67, 101 and 121 das", "unit": "das", "measured_entity": "number of emerged Striga plants for each plot", "measured_property": "recorded" } ], "split": "test", "docId": "S037842901300244X-1654", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Due to the genetic load of the pearl millet landraces used (which were never self-pollinated before), the comparatively poorer seedling establishment of the S1 materials turned out to be a disadvantage in the S1 selection process, leading to higher experimental errors (lower heritabilities) in the S1 in the C0, C2 and C4 trials, especially for ASNPC and DM (Table 1). In the C0 evaluation, S1 progenies flowered three weeks later than the FS. This was due to poorer S1 plant establishment after a severe drought that occurred after planting. Relatively high panicle yield (484 g m\u22122) was recorded in C3 in the FS, which was due to the favorable 2009 rainy season conditions. Since both S1 and FS progenies were evaluated in separate field trials, and since both S1 and FS selection schemes were combined, a real comparison of means and selection efficiencies is not possible in this study.", "measurement_extractions": [ { "quantity": "three weeks", "unit": "weeks", "measured_entity": "S1 progenies", "measured_property": "flowered" }, { "quantity": "484 g m\u22122", "unit": "g m\u22122", "measured_entity": "FS", "measured_property": "panicle yield" } ], "split": "test", "docId": "S037842901300244X-1801", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The mitochondrial machinery responsible for oxidative phosphorylation (OXPHOS) comprises five enzyme complexes containing approximately 80 proteins of which only 13 are encoded by the mitochondrial genome (mtDNA) [1]. OXPHOS deficiencies affecting a single or multiple complexes can result from mutations in either mitochondrial or nuclear genes and are associated with a variety of disease mechanisms [2,3]. With the advent of Next Generation Sequencing there is an increasing number of pathogenic mutations being identified that are not solely restricted to the 80 genes encoding OXPHOS components, thus highlighting the importance of mechanisms impacting on mitochondrial gene expression [4,5]. Combined OXPHOS deficiencies can arise from alterations in mtDNA, its maintenance [6], cardiolipin levels [7,8], or where none of these are affected, from direct defects in synthesis of mitochondrially encoded proteins [9]. This last group constitutes a heterogeneous mix of patients suffering from a wide range of clinical symptoms making clinical diagnosis difficult [10]. Genetic diagnosis is yet more elusive in children with mitochondrial disease where unidentified nuclear mutations account for the majority of cases [11]. This diagnostic problem is compounded by our relatively poor understanding of the complex molecular machinery that drives translation in mitochondria. This machinery comprises over a hundred proteins [12], all of which are putative candidate genes for translation deficiencies in human. Indeed, translation deficiencies represent a growing cause of multiple OXPHOS deficiencies with several published pathogenic mutations in genes related to the intra-organellar protein synthesis. Although many mutations associated with impaired mitochondrial translation currently map to tRNA genes [13] and a few ribosomal RNA (rRNA) [14], the list of nuclear gene mutations is steadily growing as mutations in genes encoding mitochondrial translation factors such as GFM1 (OMIM: 606639) [15,16], TSFM (OMIM: 604723) [17] and TUFM (OMIM: 602389) [18]; mitochondrial aminoacyl-tRNA synthetases (RARS2 (OMIM: 611524) [19], DARS2 (OMIM: 610956) [20], YARS2 (OMIM: 610957) [21], SARS2 (OMIM: 612804) [22], HARS2 (OMIM: 600783) [23], AARS2 (OMIM: 612035) [24], MARS2 (OMIM: 609728) [25], EARS2 (OMIM: 612799) [26]), FARS2 (OMIM: 611592) [27]; tRNA-modifying enzymes (PUS1 (OMIM: 608109) [28], TRMU (OMIM: 610230) [29], MTO1 (OMIM: 614667) [30]); other factors (C12orf65 (OMIM: 613541) [31], TACO1 (OMIM: 612958) [32], LRPPRC (OMIM: 607544) [33], C12orf62 (OMIM: 614478) [34]) and mitochondrial ribosomal proteins (MRPS16 (OMIM: 609204) [35], MRPS22 (OMIM: 605810) [36], MRPL3 (OMIM: 607118) [5]) have been successively reported (reviewed in Ref. [14]). Relatively few cases of OXPHOS deficiencies associated with mutations in mitochondrial ribosomal proteins (MRPs) have been described so far. MRPS16 mutations have been described in only one family with agenesis of corpus callosum and dysmorphism. MRPS22 mutations lead to cardiomyopathy, hypotonia and tubulopathy in a first family and Cornelia de Lange-like dysmorphic features, brain abnormalities and hypertrophic cardiomyopathy in another family. Finally, we recently identified MRPL3 mutations in four siblings of the same family presenting cardiomyopathy and psychomotor retardation. Since the mammalian mitoribosome (55S) is ~ 2 megadalton machine consisting of approximately 80 components that make up the 28S small (SSU) and 39S large subunit (LSU), it is likely that more pathogenic mutations in the constituent polypeptides will be uncovered. One of the substantial differences between the mammalian mitoribosome and those of eubacteria (70S) or the eukaryotic cytosol (80S) is the reversal in the protein to rRNA ratio. The 70S and 80S particles contain ~ 70% rRNA, whilst human mitoribosomes contain ~ 70% protein. This change in the ratio represents both an acquisition of new MRPs as well as loss of bacterial orthologues [37,38]. MRPL12 does have a bacterial orthologue, which through its interactions with translation factors is important in protein synthesis regulating both speed and accuracy [39\u201341].", "measurement_extractions": [ { "quantity": "approximately 80", "unit": null, "measured_entity": "proteins", "measured_property": null }, { "quantity": "13", "unit": null, "measured_entity": "proteins", "measured_property": null }, { "quantity": "80", "unit": null, "measured_entity": "genes", "measured_property": null }, { "quantity": "over a hundred proteins", "unit": "proteins", "measured_entity": "machinery", "measured_property": null }, { "quantity": "one", "unit": null, "measured_entity": "family", "measured_property": null }, { "quantity": "four", "unit": null, "measured_entity": "siblings of the same family", "measured_property": null }, { "quantity": "~ 2 megadalton", "unit": "megadalton", "measured_entity": "mammalian mitoribosome (55S)", "measured_property": null }, { "quantity": "80 components", "unit": "components", "measured_entity": "mammalian mitoribosome (55S)", "measured_property": null }, { "quantity": "~ 70%", "unit": "%", "measured_entity": "70S and 80S particles", "measured_property": "rRNA" }, { "quantity": "~ 70%", "unit": "%", "measured_entity": "human mitoribosomes", "measured_property": "protein" } ], "split": "test", "docId": "S0925443913001385-1319", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "RNA was isolated from fibroblasts using Trizol following manufacturer's protocol (Invitrogen). Northern blots were performed as described [48]. Briefly, aliquots of RNA (5 \u03bcg) were electrophoresed through 1.2% (w/v) agarose under denaturing conditions and transferred to GenescreenPlus membrane (NEN duPont) following the manufacturer's protocol. Radiolabelled probes were generated using random hexamers on PCR-generated templates corresponding to internal regions of the relevant genes.", "measurement_extractions": [ { "quantity": "5 \u03bcg", "unit": "\u03bcg", "measured_entity": "aliquots of RNA", "measured_property": null }, { "quantity": "1.2% (w/v)", "unit": "% (w/v)", "measured_entity": "agarose", "measured_property": null } ], "split": "test", "docId": "S0925443913001385-1526", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Human MRPL12 is 27.5% identical in amino acid sequence to the previously crystallized T. maritima L12 ribosomal protein. The C-terminal domain (CTD) (107 to end in Escherichia coli) is conserved in evolution [50] and is required for initial binding and GTPase activation for both EF-Tu and EF-G. Indeed, both EF-Tu and EF-G have greatly diminished GTPase activity on ribosomes lacking the CTD of L12 [51,52]. Superimposition of MRPL12 with the Thermus thermophilus 70S ribosome (PDB code: 2WRL) lacking L7/L12 stalk proteins shows that MRPL12 Ala181 is located within this highly conserved region (Fig. 3A). Moreover, modeling of MRPL12 shows Ala181 positioned in a helix potentially involved in translation factor interactions (Fig. 3A/B). Bacterial L7/L12 CTDs also contain a number of strictly conserved residues that are involved in the initial contact with elongation factors [52,53] and crucial for translation [54]. Alanine is one of the best helix forming residues and substitutions can therefore have profound energetic effects by perturbing packing interactions or tertiary contacts [55]. Thus, the p.Ala181Val change might be predicted to alter interactions with the elongation factors, and since MRPL7/12 bound to elongation factors is predicted to have a higher affinity for the ribosome [54], the mutation may in turn affect both rate and accuracy of mitochondrial translation.", "measurement_extractions": [ { "quantity": "27.5%", "unit": "%", "measured_entity": "Human MRPL12", "measured_property": "identical in amino acid sequence to the previously crystallized T. maritima L12 ribosomal protein" } ], "split": "test", "docId": "S0925443913001385-1621", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "MRPL12 is the orthologue of eubacterial L7/L12, where L7 is identical to L12 except that it is N-terminal acetylated. L7/L12 is also phosphorylated, which can affect both conformation and binding to partner ribosomal proteins (reviewed in Ref. [37]). This modification has now been confirmed to be present in mammalian MRPL12 [59]. In eubacteria, association of L7/12 to the large subunit takes place via the L10 protein such that two L7/L12 heterodimers normally associate per LSU [60]. Interestingly these dimers are actively exchanged on the 70S molecule without disruption of the ribosomal particle [54,61]. The dimerization status and number of dimers attached to the human 55S has not been clarified. In order to identify if the stoichiometry of MRPL12 per mt-LSU was altered as a consequence of the mutation, we performed immunoprecipitation (IP) analysis on subject and control fibroblasts using antibodies to MRPL12. Analysis of the immunoprecipitate demonstrated similar levels small subunit polypeptides including DAP3 and MRPS18B in subject and control samples. In contrast, the total amount of MRPL12 was reduced (Fig. 7B). In the patient the IP is restricted to MRPL12 in the large subunit or the fully assembled 55S with the total amount of MRPL12 being reduced as it lacks the \u201cfree\u201d population. The densitometric measurements indicate that the patient IP has ~ 49% MRPL12 compared to control, in accordance with the gradient and steady state data. ICT1 appears to be sensitive to the MRPL12 levels and so is reduced in both the IP (~ 58% of control) and in the steady state westerns (Fig. 4). The lower levels of MRPL12 could reflect loss of multimerization but since the region in the bacterial protein involved in multimerization is towards the N-terminus [62], this mutation is unlikely to have an impact on dimer/multimer formation. The translation factor bound dimer has been suggested to have an increased affinity for the ribosome [54,61]. Thus a possible explanation is that the mutation affects translation factor binding, thereby reducing the affinity of the mutant MRPL12 for the ribosome. If this were the case, however, then we would expect an increase in the pool of free MRPL12 whereas the subject exhibits a reduced pool of free MRPL12, which interacts with POLRMT [57]. The immunoprecipitation was performed using an MRPL12 specific antibody and so should contain all free MRPL12, MRPL12 associated with uncomplexed mt-LSU and MRPL12 as part of the fully assembled 55S. Since the levels of small and large subunit proteins appeared to be similar in subject and control, these data suggest that the mt-LSU and 55S assembly are unaffected by the mutation consistent with the gradient data for the protein and RNA components. Thus the reduced levels of mutant MRPL12 in this subject correspond to i) loss of stability, ii) a decrease in the free pool that is believed to interact with the mitochondrial RNA polymerase and iii) reduced translation potentially resulting from decreased interactions with translation factors, but with no detectable increase in aberrant translation products.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "L7/L12 heterodimers", "measured_property": null }, { "quantity": "~ 49%", "unit": "%", "measured_entity": "control", "measured_property": "MRPL12" }, { "quantity": "~ 58%", "unit": "%", "measured_entity": "control", "measured_property": "MRPL12 levels" } ], "split": "test", "docId": "S0925443913001385-1683", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Analysis of mitochondrial transcripts and of MRPs immunoprecipitating with MRPL12. A. Steady-state levels of mitochondrial transcripts from patient (P) and control (C) fibroblasts were analyzed by Northern blot. Signals were normalized against 18S cytosolic rRNA. Three different amounts (2, 5 and 10 \u03bcg) of total RNAs were loaded. B. MRPL12 was immunoprecipitated from mitochondrial lysates (835 \u03bcg) prepared from patient (P) and control (C) fibroblasts. Recovered MRPL12 and co-immunoprecipitating MRPs were analyzed by western blot (antibodies as previously described).", "measurement_extractions": [ { "quantity": "2, 5 and 10 \u03bcg", "unit": "\u03bcg", "measured_entity": "RNAs", "measured_property": "amounts" }, { "quantity": "835 \u03bcg", "unit": "\u03bcg", "measured_entity": "mitochondrial lysates", "measured_property": null } ], "split": "test", "docId": "S0925443913001385-849", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "P3HT (poly(3-hexylthiophene-2,5-diyl), Rieke) and PCBM ([6,6]-phenyl-C61-buyric acid methyl ester, Sigma Aldrich) were each mixed in separate vials of chlorobenzene. Both had a concentration of 41.73 mg/mL. Each vial was stirred for 2 h at 800 rpm and 60 \u00b0C in the dark. The two solutions were then mixed together and stirred overnight.", "measurement_extractions": [ { "quantity": "41.73 mg/mL.", "unit": "mg/mL", "measured_entity": "chlorobenzene", "measured_property": "P3HT (poly(3-hexylthiophene-2,5-diyl), Rieke) and PCBM ([6,6]-phenyl-C61-buyric acid methyl ester, Sigma Aldrich" }, { "quantity": "2 h", "unit": "h", "measured_entity": "Each vial", "measured_property": "stirred" }, { "quantity": "800 rpm", "unit": "rpm", "measured_entity": "Each vial", "measured_property": "stirred" }, { "quantity": "60 \u00b0C", "unit": "\u00b0C", "measured_entity": "Each vial", "measured_property": "stirred" }, { "quantity": "two", "unit": null, "measured_entity": "solutions", "measured_property": null } ], "split": "test", "docId": "S0927024813001955-812", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "PV modules are normally labelled with a power rating, which means the power measured at standard test conditions, STC, as defined in [1]. This is called peak-power, denoted as Wp. STC represent rather favourable operating conditions for most PV technologies as it is an unrealistic combination of a cold module temperature (25 \u00b0C) at a high irradiance (1000 W/m2). Different modules, even of the same technology, generally have different rated powers and the energy yields must be made comparable in any inter-comparison study. This is achieved by using the specific yield (kWh/kWp). The specific yield is a key property of PV modules at a particular location and can be a major sales argument for competing PV module suppliers.", "measurement_extractions": [ { "quantity": "25 \u00b0C", "unit": "\u00b0C", "measured_entity": "STC", "measured_property": "cold module temperature" }, { "quantity": "1000 W/m2", "unit": "W/m2", "measured_entity": "STC", "measured_property": "high irradiance" } ], "split": "test", "docId": "S0927024813002961-1085", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "It has been pointed out in the previous section that the spectrum can have a significant influence for amorphous silicon devices. The behaviour of devices will depend on the band gap of the absorber material and potentially on the window layer, where this is present. It is apparent from Fig. 7 that there is only a small spectral dependence for c-Si and CIGS devices. The absolute level depends on the normalisation, but in terms of relative changes in the generated short-circuit current, the effect accounts for about \u00b13% for CIGS and c-Si and +10% to \u221220% for a-Si devices. The overall impact on annual energy yield is slightly positive for a-Si devices and virtually zero for the other technologies.", "measurement_extractions": [ { "quantity": "about \u00b13%", "unit": "%", "measured_entity": "relative changes in the generated short-circuit current", "measured_property": "effect accounts" }, { "quantity": "+10% to \u221220%", "unit": "%", "measured_entity": "relative changes in the generated short-circuit current", "measured_property": "effect accounts" }, { "quantity": "zero", "unit": null, "measured_entity": "other technologies", "measured_property": "overall impact on annual energy yield" } ], "split": "test", "docId": "S0927024813002961-1322", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The degradation of P700 is for most devices not so significant, most of the devices have not more than 0.65%/a degradation. The only exceptions are a-Si-1 and CIGS-1. a-Si-1 was a new device at the beginning of the study, so the degradation figure includes the initial Staebler\u2013Wronski degradation despite any precondition being carried out. This means that the absolute degradation rate might be marginally affected by the initial SWE and a slightly lower rate might be observed over a longer period. The degradation of the devices is, however, higher than the rest as apparent from Fig. 10. It should be noted, however, that the PR degradation should be similarly affected but is even higher than the P700 degradation demonstrating the need for long-term energy yield estimation.", "measurement_extractions": [ { "quantity": "not more than 0.65%/a", "unit": "%/a", "measured_entity": "devices", "measured_property": "degradation" } ], "split": "test", "docId": "S0927024813002961-1357", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Great care was taken to avoid contamination of the samples by metallic impurities during the oxygen precipitation process. However, some samples were intentionally subsequently contaminated with iron, using the same procedure as in Refs. [23,34,35]. This involved rubbing the back-side of the sample with iron pieces (99.95% purity from Testbourne Limited, UK). Samples were then annealed in air in a pre-heated furnace at temperatures up to 798 \u00b0C for times chosen to ensure complete iron diffusion through the sample. Although our intention was to contaminate samples only with iron, the possibility that other transition metal impurities have entered the samples cannot be completely ruled out. Cooling was rapid, with the samples removed from the furnace at temperature and placed on a heat sink. It is estimated that samples were cooled to below \u223c100 \u00b0C in <10 s. Samples which were not intentionally contaminated with iron are referred to as \u201cuncontaminated\u201d samples in this paper, although it is possible that the low levels of impurity contamination in such samples have significant effects, as discussed later.", "measurement_extractions": [ { "quantity": "99.95%", "unit": "%", "measured_entity": "iron pieces", "measured_property": "purity" }, { "quantity": "up to 798 \u00b0C", "unit": "\u00b0C", "measured_entity": "Samples", "measured_property": "annealed in air in a pre-heated furnace at temperatures" }, { "quantity": "below \u223c100 \u00b0C", "unit": "\u00b0C", "measured_entity": "samples", "measured_property": "cooled" }, { "quantity": "<10 s", "unit": "s", "measured_entity": "samples", "measured_property": "cooled" } ], "split": "test", "docId": "S0927024813003036-1981", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Similar to the experiment schemes for comparing the efficiencies between static and incremental algorithms when deleting the objects from the universe, we also adopt such schemes to compare the performance of algorithms on the case of inserting the objects into the universe. Firstly, we compare the two algorithms, i.e., Algorithm 1 and Algorithm 3, on the six data sets in Table 5 with the same updating ratio (the ratio of the number of inserting data and original data), but different sizes of the original data. Here, we assume the updating ratio is equal to 5%. The experimental results are shown in Table 8. More detailed change trendline of each of two algorithms with the increasing size of data sets are presented in Fig. 3. Secondly, we compare the computational times of the two algorithms with the changing of updating ratios for each data sets. We show the experimental results in Table 9, and more detailed change trendline of each of two algorithms with the increasing size of data sets are given in Fig. 4.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "algorithms", "measured_property": null }, { "quantity": "six", "unit": null, "measured_entity": "data sets", "measured_property": null }, { "quantity": "5%", "unit": "%", "measured_entity": "updating ratio", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "algorithms", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "algorithms", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "algorithms", "measured_property": null } ], "split": "test", "docId": "S0950705113001895-23682", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The vertical axis wind turbine in this study is similar to the 200 kW turbine built by Vertical Wind AB [18], which has a turbine height of 24 m, diameter of 26 m and uses a permanent magnet (PM) synchronous generator.", "measurement_extractions": [ { "quantity": "200 kW", "unit": "kW", "measured_entity": "turbine", "measured_property": null }, { "quantity": "24 m", "unit": "m", "measured_entity": "turbine", "measured_property": "height" }, { "quantity": "26 m", "unit": "m", "measured_entity": "turbine", "measured_property": "diameter" } ], "split": "test", "docId": "S0960148113002735-1289", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The significant change in behaviour for the mutual topology at time 1800 s occurs when the turbine that experiences the lowest wind speed (turbine 4) stops delivering power and all turbine power is used to overcome the generator core loss. The mutual topology is unable to extract power from turbine 4 since the higher rotational velocity causes both a reduced CP (57% of the separate topology at 1800 s) and an increased core loss. The separate topology is able to extract power from turbine 4 until 2700 s due to the lower rotational velocity.", "measurement_extractions": [ { "quantity": "1800 s", "unit": "s", "measured_entity": "mutual topology", "measured_property": "time" }, { "quantity": "57%", "unit": "%", "measured_entity": "separate topology", "measured_property": "CP" }, { "quantity": "1800 s", "unit": "s", "measured_entity": "separate topology", "measured_property": null }, { "quantity": "until 2700 s", "unit": "s", "measured_entity": "separate topology", "measured_property": "extract power" } ], "split": "test", "docId": "S0960148113002735-1989", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For the square configuration, the mutual topology performed better than the separate topology. One observed issue with the separate topology is that the turbines in front easily get high rotational velocities for the peak wind speeds. It is shown in Ref. [11] that for rapid increases in wind speed, the turbine temporarily obtains a higher rotational velocity and power before the wake has formed. For rapid decreases in wind speed, the existing wake causes an additional drop in wind speed. Hence the rotational velocity and the extracted power drop below the steady state value before the wake has drifted away. For the square configuration, a rapid increase in wind speed causes the front turbine to obtain a high rotational velocity, which generates a large wake behind the turbine. This large wake will drift into the downwind turbine, hence reducing the rotational velocity of the second turbine. This effect is seen in Fig. 15 at 1050 and 1170 s, where an increase in rotational velocity for turbines 1 and 3 in the separate case is followed by a rapid decrease in rotational velocity for turbine 3. The mutual topology does not suffer from this issue, as it prevents the turbines in the front from obtaining too high rotational velocities, while keeping the rotational velocity of the turbines in the back higher.", "measurement_extractions": [ { "quantity": "1050 and 1170 s", "unit": "s", "measured_entity": "increase in rotational velocity for turbines 1 and 3", "measured_property": null } ], "split": "test", "docId": "S0960148113002735-2182", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For the following tests, the analytical solution consists of sinusoidal functions for both the velocity and the free-surface displacement:(13)uexact(x,y,t)=(\u03b70gH\u22121cos(kx\u2212gHkt)0),\u03b7exact(x,y,t)=\u03b70cos(kx\u2212gHkt),with k = \u03c0/640 m\u22121, \u03b70 = 2 m, H = 50 m, \u03bd = 3 m2 s\u22121, ct = 0, cb = 0.0025, g = 9.81 m s\u22122 and a final time of T=\u03c0/(gHk)\u224828.9s which corresponds to half a wave cycle. The computational domain \u03a9 is defined to be a rectangle of size 640 m \u00d7 320 m. Following the method of manufactured solutions, the functions (13) are substituted into the shallow water equations (3) and the non-zero remainders are added as source terms. This ensures that (13) is a solution of this modified system.", "measurement_extractions": [ { "quantity": "2 m", "unit": "m", "measured_entity": "\u03b70", "measured_property": null }, { "quantity": "50 m", "unit": ",", "measured_entity": "H", "measured_property": null }, { "quantity": "3 m2 s\u22121", "unit": "m2 s\u22121", "measured_entity": "\u03bd", "measured_property": null }, { "quantity": "0", "unit": null, "measured_entity": "ct", "measured_property": null }, { "quantity": "0.0025", "unit": null, "measured_entity": "cb", "measured_property": null }, { "quantity": "9.81 m s\u22122", "unit": "m s\u22122", "measured_entity": "g", "measured_property": null }, { "quantity": "\u224828.9s", "unit": "s", "measured_entity": "T", "measured_property": null }, { "quantity": "640 m \u00d7 320 m.", "unit": "m", "measured_entity": "rectangle", "measured_property": "size" }, { "quantity": "non-zero", "unit": null, "measured_entity": "remainders", "measured_property": null } ], "split": "test", "docId": "S0960148113004989-2841", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Site conditions: imperfections in a turbine's surroundings are not considered in our model; for example: turbulence intensity, terrain slope, blockage effects, blade fouling (by dirt, ice, insects, etc.), or masking by surrounding terrain. These impacts are highly site specific and hard to quantify with a single factor, with the only source we found estimating that they reduce output by 2\u20135%, plus 1% per 3% increase in turbulence intensity [36].", "measurement_extractions": [ { "quantity": "2\u20135%", "unit": "%", "measured_entity": "output", "measured_property": "reduce" }, { "quantity": "1%", "unit": "%", "measured_entity": "output", "measured_property": "reduce" }, { "quantity": "3%", "unit": "%", "measured_entity": "turbulence intensity", "measured_property": "increase" } ], "split": "test", "docId": "S0960148113005727-1203", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Fig. 9a highlights these first two reasons as a gradual downwards slope in the bulk of weather-corrected load factor observations, and isolated periods of very low output due to downtime which are concentrated towards higher ages. Fig. 3a shows an example of the third: Blyth Harbour stands out at the lower-right of the chart as its load factor declined from 12% to just 2% between the ages of 12 and 17. By the end of its life only one of the nine turbines was generating, giving it the worst degradation rate of the farms we observed.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "reasons", "measured_property": null }, { "quantity": "from 12% to just 2%", "unit": "%", "measured_entity": "Blyth Harbour", "measured_property": "load factor" }, { "quantity": "between the ages of 12 and 17", "unit": null, "measured_entity": "Blyth Harbour", "measured_property": null }, { "quantity": "nine", "unit": null, "measured_entity": "turbines", "measured_property": null } ], "split": "test", "docId": "S0960148113005727-1451", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The cumulative lifetime output of a 100 MW wind farm with a 28.5% load factor would be 4.99 TWh over 20 years. If this farm suffers a linear annual deterioration of \u22120.41 points after the first year, its lifetime output reduces to 4.37 TWh, a fall of 12.5%. This will increase the cost of electricity from wind generators, as less electricity is produced to recover the costs of construction. The economic value of the lost output is relatively low as it mostly occurs in the far future. With a discount rate of 10%, degradation increases the levelised cost of electricity by 9%, from approximately \u00a390 [4,5] to \u00a398 per MWh. This impact becomes greater if the economic lifetime increases or the discount rate decreases.", "measurement_extractions": [ { "quantity": "100 MW", "unit": "MW", "measured_entity": "wind farm", "measured_property": null }, { "quantity": "28.5%", "unit": "%", "measured_entity": "100 MW wind farm", "measured_property": "load factor" }, { "quantity": "4.99 TWh", "unit": "TWh", "measured_entity": "wind farm", "measured_property": "cumulative lifetime output" }, { "quantity": "over 20 years", "unit": "years", "measured_entity": "wind farm", "measured_property": "cumulative lifetime output" }, { "quantity": "\u22120.41 points", "unit": "points", "measured_entity": "farm", "measured_property": "linear annual deterioration" }, { "quantity": "4.37 TWh", "unit": "TWh", "measured_entity": "farm", "measured_property": "lifetime output" }, { "quantity": "12.5%", "unit": "%", "measured_entity": "farm", "measured_property": "lifetime output" }, { "quantity": "10%", "unit": "%", "measured_entity": "discount rate", "measured_property": null }, { "quantity": "9%", "unit": "%", "measured_entity": "electricity", "measured_property": "levelised cost" }, { "quantity": "from approximately \u00a390 [4,5] to \u00a398 per MWh", "unit": "per MWh", "measured_entity": "electricity", "measured_property": "levelised cost" } ], "split": "test", "docId": "S0960148113005727-1466", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Histograms summarising the vintage of UK wind farms in our dataset (a), the distribution of monthly observations from this fleet (b\u2013c), and the distribution of load factors for individual farms over the last eleven years (d\u2013e).", "measurement_extractions": [ { "quantity": "eleven", "unit": null, "measured_entity": "years", "measured_property": null } ], "split": "test", "docId": "S0960148113005727-739", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "By accounting for individual site conditions we confirm that load factors do decline with age, at a similar rate to other rotating machinery. Wind turbines are found to lose 1.6 \u00b1 0.2% of their output per year, with average load factors declining from 28.5% when new to 21% at age 19. This trend is consistent for different generations of turbine design and individual wind farms. This level of degradation reduces a wind farm's output by 12% over a twenty year lifetime, increasing the levelised cost of electricity by 9%.", "measurement_extractions": [ { "quantity": "1.6 \u00b1 0.2%", "unit": "%", "measured_entity": "Wind turbines", "measured_property": "output per year" }, { "quantity": "28.5%", "unit": null, "measured_entity": "Wind turbines", "measured_property": "average load factors" }, { "quantity": "21%", "unit": null, "measured_entity": "Wind turbines", "measured_property": "average load factors" }, { "quantity": "19", "unit": null, "measured_entity": "Wind turbines", "measured_property": "age" }, { "quantity": "12%", "unit": "%", "measured_entity": "wind farm's output", "measured_property": "degradation reduces" }, { "quantity": "twenty year", "unit": "year", "measured_entity": "wind farm's", "measured_property": "lifetime" }, { "quantity": "9%", "unit": "%", "measured_entity": "wind farm's", "measured_property": "levelised cost of electricity" } ], "split": "test", "docId": "S0960148113005727-855", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Rates of oxygen consumption were assessed in coral fragments placed within 220 mL incubation chambers fitted with oxygen optodes connected to a temperature-compensated oxygen analyser (Oxy-4 Mini with Temp-4, Presens & Loligo systems). Magnetic stirrers ensured homogeneity of oxygen around the coral fragments. Chambers were filled with tank seawater and corals were allowed to acclimate to the conditions for 2 h. Ten chambers were used per treatment at each time point; 8 for respiration, and 2 as seawater \u2018blanks\u2019 in order to measure (and subsequently subtract) background microbial respiration. Prior to respiration measures, corals were not fed for 48 h. Once chamber lids were attached ensuring no headspace, oxygen consumption was recorded for a 40-min period for each fragment, during which oxygen saturation did not fall below 80%. Following incubations at T+21 days, fragments were removed and preserved at \u221220 \u00b0C for subsequent weight determination.", "measurement_extractions": [ { "quantity": "220 mL", "unit": "mL", "measured_entity": "incubation chambers", "measured_property": null }, { "quantity": "2 h", "unit": "h", "measured_entity": "corals", "measured_property": "acclimate" }, { "quantity": "Ten", "unit": null, "measured_entity": "chambers", "measured_property": null }, { "quantity": "8", "unit": null, "measured_entity": "chambers", "measured_property": null }, { "quantity": "2", "unit": null, "measured_entity": "chambers", "measured_property": null }, { "quantity": "48 h", "unit": "h", "measured_entity": "corals", "measured_property": "not fed" }, { "quantity": "40-min", "unit": "min", "measured_entity": "oxygen consumption", "measured_property": "recorded" }, { "quantity": "below 80%", "unit": "%", "measured_entity": "oxygen saturation", "measured_property": "did not fall" }, { "quantity": "T+21 days", "unit": "days", "measured_entity": "fragments", "measured_property": "removed" }, { "quantity": "\u221220 \u00b0C", "unit": "\u00b0C", "measured_entity": "fragments", "measured_property": "preserved" } ], "split": "test", "docId": "S0967064513002774-1376", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Kissinger plot of the temperatures Tp of maximum length change rate (dilatometry: \u2022) or heat release rate (DSC: \u25be) measured on HPT-Cu applying different heating rates \u03b4 in the range from 1.25 to 50 K min-1.", "measurement_extractions": [ { "quantity": "1.25 to 50 K min-1", "unit": "K min-1", "measured_entity": "heating rates", "measured_property": null } ], "split": "test", "docId": "S1359645413009816-1712", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For the DSC measurements, 14 samples, taken from three different positions of the HPT disk, were prepared. Six samples were cut from the disk at a radius of r=9mm at different heights. Four samples were prepared at radii of r = 9.3 and r = 7.3 mm. The measurements were performed with a Perkin Elmer DSC7 differential calorimeter, which determines the heat release for the annealing processes at different linear heating rates. A subsequent re-run served as the reference measurement and baseline for the analysis (for details see Ref. [13]).", "measurement_extractions": [ { "quantity": "14", "unit": null, "measured_entity": "samples", "measured_property": null }, { "quantity": "three", "unit": null, "measured_entity": "positions of the HPT disk", "measured_property": null }, { "quantity": "Six", "unit": null, "measured_entity": "samples", "measured_property": null }, { "quantity": "9mm", "unit": "mm", "measured_entity": "Six samples", "measured_property": "radius" }, { "quantity": "9.3 and r = 7.3 mm", "unit": "mm", "measured_entity": "Four samples", "measured_property": "radii" } ], "split": "test", "docId": "S1359645413009816-2227", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "In the Ivanova et al. (2012) study the impact of reservoir temperature on the mass estimation was not included. However, temperature is known to have a significant effect on CO2 density and can presumably have an effect on the mass estimation based on the time-lapse seismic data. Fig. 3 illustrates the dependence of CO2 density on pressure and temperature in the reservoir at Ketzin. Prior to the start of the CO2 injection, pressure and temperature at the injection horizon were approximately 6.2 MPa and 34 \u00b0C, respectively. Both pressure and temperature increased due to the injection (Giese et al., 2009; W\u00fcrdemann et al., 2010). In October 2009, pressure and temperature reached values of approximately 7.73 MPa and 38 \u00b0C, respectively, in the injection well Ktzi201/2007 at the injection depth. At the observation well Ktzi200/2007, the temperature increased only slightly. There was no significant change in the values of the reservoir temperature at the observation well Ktzi202/2007 (Fig. 2) (M\u00f6ller et al., 2012). Based on these observations, it appears likely that the CO2 density was around 260 kg/m3 at the injection point (38 \u00b0C) in autumn 2009, whereas it was near 320 kg/m3 in the more distant part of the plume, close to the ambient temperature (34 \u00b0C) (Fig. 3).", "measurement_extractions": [ { "quantity": "approximately 6.2 MPa", "unit": "MPa", "measured_entity": "reservoir at Ketzin", "measured_property": "pressure" }, { "quantity": "34 \u00b0C", "unit": "\u00b0C", "measured_entity": "reservoir at Ketzin", "measured_property": "temperature" }, { "quantity": "approximately 7.73 MPa", "unit": "MPa", "measured_entity": "reservoir at Ketzin", "measured_property": "pressure" }, { "quantity": "38 \u00b0C", "unit": "\u00b0C", "measured_entity": "reservoir at Ketzin", "measured_property": "temperature" }, { "quantity": "October 2009,", "unit": null, "measured_entity": "pressure and temperature", "measured_property": "reached values of approximately 7.73 MPa and 38 \u00b0C" }, { "quantity": "around 260 kg/m3", "unit": "kg/m3", "measured_entity": "CO2", "measured_property": "density" }, { "quantity": "38 \u00b0C", "unit": "\u00b0C", "measured_entity": "injection point", "measured_property": null }, { "quantity": "autumn 2009", "unit": null, "measured_entity": "CO2 density", "measured_property": "around 260 kg/m3 at the injection point (38 \u00b0C)" }, { "quantity": "near 320 kg/m3", "unit": "kg/m3", "measured_entity": "CO2", "measured_property": "density" }, { "quantity": "34 \u00b0C", "unit": "\u00b0C", "measured_entity": "ambient temperature", "measured_property": null } ], "split": "test", "docId": "S175058361300203X-1240", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "To prevent ocean acidification and mitigate greenhouse gas emissions, it is necessary to capture and store carbon dioxide. The Sleipner storage site, offshore Norway, is the world's first and largest engineered waste repository for a greenhouse gas. CO2 is separated from the Sleipner gas condensate field and stored in the pore space of the Utsira Formation, a saline aquifer approximately 1 km below the surface and 200 km from the coast. Statoil, the field operator, has injected almost 1 Mt/yr of captured CO2 into the storage site since 1996. The buoyant CO2 plume ascended rapidly through eight thin shale barriers within the aquifer to reach the top seal in less than three years. The plume's progress has been monitored by eight seismic surveys, as well as gravimetric and electromagnetic monitoring, which record the spreading of nine thin CO2 layers. This paper presents a capillary flow model using invasion percolation physics that accurately matches the plume's geometry. The approach differs from standard Darcy flow simulations, which fail to match the plume geometry. The calibrated capillary flow simulation indicates that a mass balance for the plume is likely, but can only replicate the plume geometry if the thin intra-formational shale barriers are fractured. The model enables an estimate of the shale barrier behavior and caprock performance. The fractures are very unlikely to have been caused by CO2 injection given the confining stress of the rock and weak overpressure of the plume, and so fracturing must pre-date injection. A novel mechanism is suggested: the deglaciation of regional ice sheets that have rapidly and repeatedly unloaded approximately 1 km of ice. The induced transient pore pressures are sufficient to hydro-fracture thin shales. The fractures enable fast CO2 ascent, resulting in a multi-layered plume. Shallow CO2 storage sites in the Northern North Sea and other regions that have been loaded by Quaternary ice sheets are likely to behave in a similar manner.", "measurement_extractions": [ { "quantity": "approximately 1 km", "unit": "km", "measured_entity": "saline aquifer", "measured_property": "below the surface" }, { "quantity": "200 km", "unit": "km", "measured_entity": "saline aquifer", "measured_property": "from the coast" }, { "quantity": "almost 1 Mt/yr", "unit": "Mt/yr", "measured_entity": "captured CO2", "measured_property": "injected" }, { "quantity": "since 1996.", "unit": null, "measured_entity": "captured CO2", "measured_property": "injected" }, { "quantity": "eight thin shale barriers", "unit": "thin shale barriers", "measured_entity": "buoyant CO2 plume", "measured_property": "ascended rapidly through" }, { "quantity": "less than three years", "unit": "years", "measured_entity": "buoyant CO2 plume", "measured_property": "reach the top seal" }, { "quantity": "eight", "unit": null, "measured_entity": "seismic surveys", "measured_property": null }, { "quantity": "nine", "unit": null, "measured_entity": "thin CO2 layers", "measured_property": null }, { "quantity": "approximately 1 km", "unit": "km", "measured_entity": "ice", "measured_property": null } ], "split": "test", "docId": "S1750583613004192-1126", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The modeled low shale barrier threshold pressures are potentially indicative of fracturing. However, the current pressure regime is extremely unlikely to cause fracturing, and so it is inferred that the shales may have been pervasively fractured in the past. A fracturing process consistent with the geological history of the region is that the shale barriers have undergone a transient pulse of fluid overpressure sufficient to fracture the rock. Such a pressure history could be a consequence of the rapid unloading of hundreds of meters of ice from the overburden during a deglaciation. A transient ice sheet load equivalent to 10 MPa of overpressure could have been removed rapidly by melting, or instantly by a sea level rise and the flotation of an ice sheet grounded on the seabed, during a typical ablation timespan of less than a few thousand years. At the shallow burial depth of these shales, 10 MPa is sufficient to exceed the tensional rock strength and cause pervasive hydraulic fracturing. This may have happened as a consequence of one or more regional ice sheet excursions into the basin. While the strength and thickness of the overburden at any given ice collapse is unknown, a conservative approximation for the fracture gradient, based on present-day leak-off tests (Nicoll, 2012), suggests that such an event is plausible.", "measurement_extractions": [ { "quantity": "hundreds of meters", "unit": "meters", "measured_entity": "ice", "measured_property": null }, { "quantity": "10 MPa", "unit": "MPa", "measured_entity": "transient ice sheet load", "measured_property": "overpressure" }, { "quantity": "less than a few thousand years", "unit": "years", "measured_entity": "ablation", "measured_property": "timespan" }, { "quantity": "10 MPa", "unit": "MPa", "measured_entity": "transient ice sheet load", "measured_property": "overpressure" } ], "split": "test", "docId": "S1750583613004192-1689", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Using the optimized operating conditions we demonstrate serial passaging and expansion of two integration-free hiPSC lines in completely defined xeno-free media in 100-ml bioreactors. The cells exhibited homogeneous aggregate formation, steady expansion in their pluripotent state, and normal karyotype after ~ 20 passages. Note that one experiment of extended expansion of TNC1 cells in spinner flask (p30 + 6 + 21) showed abnormal karyotype (47, XXY). However, there is no evidence that it should be attributed to the suspension culture. In general, recently improved iPSC culture conditions reduce selective pressure for the growth of mutated cells that acquire growth advantages. Although karyotypically abnormal cells were found occasionally after long-term cultures under both adherent and suspension conditions, the frequency is low (5% of tested batches of hiPSCs and hESCs). The standard karyotyping or other genotyping methods like what we performed here will remain necessary to manage this inherent issue of long-term cell cultures.", "measurement_extractions": [ { "quantity": "two", "unit": null, "measured_entity": "integration-free hiPSC lines", "measured_property": null }, { "quantity": "100-ml", "unit": "ml", "measured_entity": "bioreactors", "measured_property": null }, { "quantity": "~ 20", "unit": null, "measured_entity": "passages", "measured_property": null }, { "quantity": "5%", "unit": "%", "measured_entity": "tested batches of hiPSCs and hESCs", "measured_property": "karyotypically abnormal cells" } ], "split": "test", "docId": "S1873506113001116-1456", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "ALP staining was performed with ALP substrate (1-Step NBT/BCIP; Pierce, IL, USA) after fixation with 10% neutral buffered formalin solution (Wako Chemical), as previously described (Fusaki et al., 2009; Ban et al., 2011).", "measurement_extractions": [ { "quantity": "10%", "unit": "%", "measured_entity": "neutral buffered formalin solution", "measured_property": null } ], "split": "test", "docId": "S1873506114000075-1104", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Efficient generation of FAP-specific iPS cells by SeV vectors. (A) ALP staining of cells growing on 100-mm dishes. (B) RT-PCR analysis of expression of NANOG, TERT, SeV, and \u03b2-actin. iPS-1 to iPS-10 are established clones. (C) Typical staining of FAP-specific iPS cell colonies with anti-SeV antibodies. The colony was partially positive (left panel) and negative (right panel) for SeV. BC, bright contrast. Scale bar, 50 \u03bcm and 100 \u03bcm. (D) Immunostaining of established clones with pluripotency marker Oct3/4 (green). Nuclei were stained with DAPI (blue). BF, bright field. Scale bar, 100 \u03bcm.", "measurement_extractions": [ { "quantity": "100-mm", "unit": "mm", "measured_entity": "dishes", "measured_property": null }, { "quantity": "50 \u03bcm and 100 \u03bcm", "unit": "\u03bcm", "measured_entity": "Scale bar", "measured_property": null }, { "quantity": "100 \u03bcm", "unit": "\u03bcm", "measured_entity": "Scale bar", "measured_property": null } ], "split": "test", "docId": "S1873506114000075-665", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(F) Immunofluorescence analysis of pluripotency markers in control iPSC line 20, sporadic iPSC line 9, and PGRN S116X iPSC line 26, and their respective normal karyotypes. Cell nuclei were counterstained with DAPI (blue). Scale bar, 50 \u03bcm.", "measurement_extractions": [ { "quantity": "50 \u03bcm", "unit": "\u03bcm", "measured_entity": "Scale bar", "measured_property": null } ], "split": "test", "docId": "S2211124712002884-620", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Scale bars in (F)\u2013(H) correspond to 50 \u03bcm. See also Figure S2 and Tables S1 and S2.", "measurement_extractions": [ { "quantity": "50 \u03bcm", "unit": "\u03bcm", "measured_entity": "Scale bars in (F)\u2013(H)", "measured_property": null } ], "split": "test", "docId": "S2211124713006475-741", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Bilateral 50 and 100 ms STG activity was observed in both groups. HC had stronger bilateral 50 and 100 ms STG activity than SZ. In addition to the STG group difference, non-STG activity was also observed in both groups. For example, whereas HC had stronger left and right inferior frontal gyrus activity than SZ, SZ had stronger right superior frontal gyrus and left supramarginal gyrus activity than HC.", "measurement_extractions": [ { "quantity": "50 and 100 ms", "unit": "ms", "measured_entity": "STG", "measured_property": "activity" }, { "quantity": "50 and 100 ms", "unit": "ms", "measured_entity": "STG", "measured_property": "activity" } ], "split": "test", "docId": "S2213158213000582-1041", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "\u2022Auditory encoding in schizophrenia (SZ) and healthy controls (HC) was examined.\u2022Distributed source localization provided whole-brain measures from 30 to 130 ms.\u2022Abnormalities were observed in superior temporal gyrus (STG) auditory areas in SZ.\u2022Encoding abnormalities were also observed in frontal and supramarginal gyrus areas.\u2022SZ shows abnormalities in multiple nodes of a distributed auditory network.", "measurement_extractions": [ { "quantity": "30 to 130 ms", "unit": "ms", "measured_entity": "whole-brain measures", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-1050", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Using electroencephalography (EEG) and magnetoencephalography (MEG), a now large number of studies show smaller 100 ms auditory amplitudes in individuals with schizophrenia (SZ) than healthy controls (HC). In a review of studies examining N1 and M100 in schizophrenia, Rosburg et al. (2008) concluded that 100 ms auditory abnormalities are most commonly observed in studies using interstimulus intervals greater than 1 s and that an increase in N1 amplitude by allocation of attention is often lacking in individuals with SZ. Several large-sample studies provide examples. Examining N1 activity in the standard paired-click paradigm, Turetsky et al. (2008) observed a small first and a normal second N1 click response in SZ (N = 142) relative to HC (N = 221). Reduced N1 was also observed in the unaffected first-degree relatives of individuals with SZ without co-morbid psychiatric or substance use conditions, and N1 amplitude was observed to be a heritable measure and a better endophenotype than N1 gating. In another recent large-N study, Smith et al. (2010) used simultaneous EEG and MEG to examine 100 ms auditory processes in individuals with SZ (N = 79) and HC (N = 73) during a paired-click task. Patients had larger N1 Cz and left and right superior temporal gyrus (STG) M100 ratio scores (second-click/first-click), with EEG and MEG ratio score group differences due to a smaller first click (S1) response in patients, suggesting a deficit in encoding auditory information rather than a deficit in filtering redundant information.", "measurement_extractions": [ { "quantity": "100 ms", "unit": "ms", "measured_entity": "individuals with schizophrenia", "measured_property": "auditory amplitudes" }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "auditory abnormalities", "measured_property": null }, { "quantity": "greater than 1 s", "unit": "s", "measured_entity": "interstimulus intervals", "measured_property": null }, { "quantity": "142", "unit": null, "measured_entity": "SZ", "measured_property": "N" }, { "quantity": "221", "unit": null, "measured_entity": "HC", "measured_property": "N" }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "auditory processes", "measured_property": null }, { "quantity": "79", "unit": null, "measured_entity": "SZ", "measured_property": "N" }, { "quantity": "73", "unit": null, "measured_entity": "HC", "measured_property": "N" } ], "split": "test", "docId": "S2213158213000582-1183", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The paired-click paradigm followed the protocol of Adler et al. (1993), in which 3 ms binaural clicks were presented in pairs (S1 and S2) with 500 ms inter-stimulus interval and with inter-trial interval jitter between 7 and 11 s, averaging 9 s. Clicks were delivered through earphones placed in each ear canal. The peak intensity of the click was presented 35 dB above each subject's hearing threshold. Presenting 150 click trials, the duration of the task was approximately 25 minutes. As previously noted, the present study examined only S1 activity at 50 and 100 ms.", "measurement_extractions": [ { "quantity": "3 ms", "unit": "ms", "measured_entity": "binaural clicks", "measured_property": null }, { "quantity": "500 ms", "unit": "ms", "measured_entity": "binaural clicks", "measured_property": "inter-stimulus interval" }, { "quantity": "between 7 and 11 s", "unit": "s", "measured_entity": "binaural clicks", "measured_property": "inter-trial interval jitter" }, { "quantity": "averaging 9 s", "unit": "s", "measured_entity": "binaural clicks", "measured_property": "inter-trial interval jitter" }, { "quantity": "35 dB", "unit": "dB", "measured_entity": "click", "measured_property": "peak intensity" }, { "quantity": "150", "unit": null, "measured_entity": "click trials", "measured_property": null }, { "quantity": "approximately 25 minutes", "unit": "minutes", "measured_entity": "task", "measured_property": "duration" }, { "quantity": "50 and 100 ms", "unit": "ms", "measured_entity": "S1 activity", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-1279", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "MEG raw signals were first processed with Signal Space Separation (SSS; Taulu et al., 2004) using Maxfilter (Elekta MaxfilterTM; Elekta Oy). SSS separates neuronal magnetic signals arising from inside the MEG sensor array from external magnetic signals arising from the surrounding environment to effectively reduce environmental noise and artifacts. After SSS, S1 epochs 500 ms pre-stimulus to 500 ms post-stimulus were averaged. Trials containing eye-blinks and large eye-movements were excluded. On average, 103 trials were obtained for each subject, and there were no group differences in number of accepted trials (t(39) = 0.30, p = 0.77).", "measurement_extractions": [ { "quantity": "500 ms", "unit": "ms", "measured_entity": "S1 epochs", "measured_property": null }, { "quantity": "500 ms", "unit": "ms", "measured_entity": "S1 epochs", "measured_property": null }, { "quantity": "average, 103", "unit": null, "measured_entity": "trials", "measured_property": null }, { "quantity": "0.30", "unit": null, "measured_entity": "t(39)", "measured_property": null }, { "quantity": "0.77", "unit": null, "measured_entity": "p", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-1309", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Group contrast maps at 50 ms (Fig. 3) showed group differences in STG and non-STG regions (FDR q < 0.05). HC had stronger left and right STG (L-STG, R-STG), right inferior temporal gyrus (R-ITG), and left and right IFG (L-IFG, R-IFG) 50-ms activity than SZ. SZ had stronger right superior frontal gyrus (R-SFG) and left supramarginal gyrus (L-SMG) 50 ms activity than HC.", "measurement_extractions": [ { "quantity": "50 ms", "unit": "ms", "measured_entity": "Group contrast maps", "measured_property": null }, { "quantity": "0.05", "unit": null, "measured_entity": "FDR", "measured_property": "q" }, { "quantity": "50-ms", "unit": "ms", "measured_entity": "left and right IFG (L-IFG, R-IFG)", "measured_property": "activity" }, { "quantity": "50 ms", "unit": "ms", "measured_entity": "left supramarginal gyrus (L-SMG)", "measured_property": "activity" } ], "split": "test", "docId": "S2213158213000582-1390", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Similar to the 50 ms group maps, group contrast maps at 100 ms (Fig. 4) showed group differences in STG and non-STG regions (FDR q < 0.05). HC had stronger activity in L-STG, R-STG, R-ITG, R-IFG, and the posterior part of right SFG (R-SFG_p)/SMA than SZ at 100 ms. SZ had stronger activity in R-SFG, posterior part of left SFG (L-SFG_p)/SMA, and L-SMG than HC at 100 ms.", "measurement_extractions": [ { "quantity": "50 ms", "unit": "ms", "measured_entity": "group maps", "measured_property": null }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "group contrast maps", "measured_property": null }, { "quantity": "< 0.05", "unit": null, "measured_entity": "group differences in STG and non-STG regions", "measured_property": "FDR q" }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "group contrast maps", "measured_property": null }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "group contrast maps", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-1398", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "The present study shows that STG and non-STG areas (e.g., frontal regions) are involved in early auditory encoding. Replicating findings from our prior study that used single dipole source localization to examine STG activity (Smith et al., 2010) and now examining a new sample using a different MEG system and applying distributed source localization methods, reduced S1 STG activity was observed in SZ, again supporting impaired encoding of auditory information. Whereas in Smith et al. we observed left 50 ms and bilateral 100 ms STG group differences, group differences were observed bilaterally at both 50 and 100 ms in this new sample.", "measurement_extractions": [ { "quantity": "50 ms", "unit": "ms", "measured_entity": "STG group differences", "measured_property": null }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "STG group differences", "measured_property": null }, { "quantity": "50 and 100 ms", "unit": "ms", "measured_entity": "group differences", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-1410", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "As noted in the Introduction, Naatanen and Picton (1987) argued that the electric N1 reflects contributions from frontal sources as well as from primary auditory cortex. Present results provide confirmation of these findings, with frontal activity observed in both groups (Fig. 2). Findings are also consistent with imaging studies that have observed frontal activity during auditory tasks.22Although many paired-click studies have examined the association between frontal activity and gating measures (e.g., second click divided by first click), studies examining associations between paired-click ratio scores and frontal activity but not also associations between first click activity and frontal activity are not discussed as findings between ratio scores and frontal activity are outside the scope of this study (e.g., EEG studies such as Williams et al. (2011) and fMRI studies such as Tregellas et al. (2007) and Mayer et al. (2009)). For example, examining auditory responses in patients with epilepsy using intracranial microelectrode grids, Korzyukov et al. (2007) detected 50 ms temporal and frontal auditory activity, findings consistent with earlier corticography studies suggesting frontal contributions to P50 (Grunwald et al., 2003). Boutros et al. (2011) also detected 100 ms temporal and frontal auditory activity using subdural electrodes. Similar to present findings in controls, Boutros et al. observed activity in the posterior part of STG and in left ventral prefrontal cortex (more exact comparisons between the two studies in terms of frontal activity are difficult, as many subjects in Boutros et al. did not have electrodes placed in anterior frontal regions). Boutros et al. (2011) also observed 100 ms auditory activity in middle temporal gyrus, parietal, cingulate, and occipital regions. Activity observed in similar but not identical locations in these other areas in the present study may be due to the significant latency variability Boutros et al. (2011) observed in several regions. Variability across subjects in the location of activation may also account for study differences.", "measurement_extractions": [ { "quantity": "50 ms", "unit": "ms", "measured_entity": "temporal and frontal", "measured_property": "auditory activity" }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "temporal and frontal", "measured_property": "auditory activity" }, { "quantity": "two", "unit": null, "measured_entity": "studies", "measured_property": null }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "middle temporal gyrus, parietal, cingulate, and occipital regions", "measured_property": "auditory activity" } ], "split": "test", "docId": "S2213158213000582-1424", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "A limitation of the present study is that most subjects were chronic patients and all patients were on medication. As such, it is not possible to determine whether the observed abnormalities are observed only in chronic patients or whether group differences were due to medication. Several studies, however, show that 100 ms abnormalities in schizophrenia are present at first onset as well as in first-degree relatives (Turetsky et al., 2008), suggesting that auditory abnormalities in SZ are not due to medication. In addition, in a review of 100 ms auditory studies Rosburg et al. (2008) concluded that medication did not seem to account for group differences in N100 activity.", "measurement_extractions": [ { "quantity": "100 ms", "unit": "ms", "measured_entity": "abnormalities in schizophrenia", "measured_property": null }, { "quantity": "100 ms", "unit": "ms", "measured_entity": "auditory studies", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-1469", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Between-group analyses for 50 ms activity. Activation clusters in yellow/red (thresholded at FDR q < 0.05) show stronger activity in HC than SZ (HC > SZ). Activation clusters (thresholded at FDR q < 0.05) in blue show stronger activity in SZ than HC (SZ > HC). The effect sizes for L-STG, R-Frontal and R-SFG M50 measures are provided in Table 2.", "measurement_extractions": [ { "quantity": "50 ms", "unit": "ms", "measured_entity": "activity", "measured_property": null }, { "quantity": "0.05", "unit": null, "measured_entity": "FDR", "measured_property": "q" }, { "quantity": "0.05", "unit": null, "measured_entity": "FDR", "measured_property": "q" } ], "split": "test", "docId": "S2213158213000582-751", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Between-group analyses for 100 ms activity. Activation clusters in yellow/red show stronger activity in HC than SZ (HC > SZ). Activation clusters in blue show stronger activity in SZ than HC (SZ > HC). The effect sizes for L-STG, R-Frontal and R-SFG M100 measures are provided in Table 2.", "measurement_extractions": [ { "quantity": "100 ms", "unit": "ms", "measured_entity": "activity", "measured_property": null } ], "split": "test", "docId": "S2213158213000582-766", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Performances of the three classifiers are shown in Table 1, and receiver operating curves (ROCs) are plotted in Fig. 5. While the sMRI classifier and fMRI classifier performed well individually, their combination achieved a significant improvement in performance. The combined classifier yielded AUC and EER as high as 0.90 and 0.89, respectively. From the ROC, we can see that the sMRI classifier could not predict some of the positive subjects (HI) correctly even when the decision threshold was set to be very low, because the classifier did not reach 100% true positive rate even when the false positive rate approached 100%. However, the ROC for fMRI was in an opposite situation. The ROC did not reach 0% false positive rate even when the true positive rate approached 0%, suggesting that the fMRI classifier had difficulty in classifying some of the negative subjects (NH) correctly. As sMRI and fMRI classifiers were vulnerable to different types of errors, it was possible to combine them to overcome their individual limitations. To illustrate the reason why the combination can be successful, we plotted sMRI\u2013fMRI scores in Fig. 6 and S1. Simply speaking, the fMRI classifier draws a horizontal line to separate the two groups of subjects based on the fMRI data, while the sMRI classifier draws a vertical line to separate the two groups based on the sMRI data. Obviously, the two groups could not be perfectly separated by either a horizontal or a vertical line in Fig. 6 and S1. However, by combining the fMRI and sMRI classifiers, the two groups of subjects were separable with a diagonal line as shown in the figures.", "measurement_extractions": [ { "quantity": "three", "unit": null, "measured_entity": "classifiers", "measured_property": null }, { "quantity": "0.90", "unit": null, "measured_entity": "combined classifier", "measured_property": "AUC" }, { "quantity": "0.89", "unit": null, "measured_entity": "combined classifier", "measured_property": "EER" }, { "quantity": "100%", "unit": "%", "measured_entity": "sMRI classifier", "measured_property": "true positive rate" }, { "quantity": "approached 100%", "unit": "%", "measured_entity": "sMRI classifier", "measured_property": "false positive rate" }, { "quantity": "0%", "unit": "%", "measured_entity": "ROC for fMRI", "measured_property": "false positive rate" }, { "quantity": "approached 0%", "unit": "%", "measured_entity": "ROC for fMRI", "measured_property": "true positive rate" }, { "quantity": "two", "unit": null, "measured_entity": "groups of subjects", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "groups", "measured_property": null }, { "quantity": "two", "unit": null, "measured_entity": "groups of subjects", "measured_property": null } ], "split": "test", "docId": "S2213158213001253-2433", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Although computer-aided diagnosis of hearing loss is not needed, our algorithm can potentially advance the study of congenital hearing loss mechanism by identifying discriminative brain regions as disease biomarkers for hearing impairment at various levels in the auditory system. Inspecting the most important features that differentiate children born with hearing impairment from children with normal hearing in this study, we see some features that are in line with hypotheses about under stimulation of auditory function in HI infants; while other observations already begin to add to our knowledge of how congenital deafness affects brain development and function. For example, features B, F, H, and I include known components of the auditory language network which our group and others have previously shown to be engaged by the narrative comprehension task (Karunanayaka et al., 2007; Schmithorst et al., 2006). These features include (B) the planum temporale and primary auditory cortex in the left hemisphere (including Wernicke's area, the classical language recognition module), as well as the angular gyrus and supramarginal gyrus at the temporal parietal junction of the (F) left and (H, I) right hemispheres, known auditory and visual language association regions. Although all participants were bilaterally severely to profoundly hearing impaired, we observe left dominant auditory/language related activity present in components A, B, and F. In addition, components H and I contain right hemisphere auditory/language activity. Functional features such as these are not unexpected in terms of regions of differential cortical activation between HI and NH children listening to natural language as an auditory stimulus and it is reassuring to see these regions highlighted by our algorithm as potential biomarkers corresponding to hearing impairment. Similarly, there is evidence of differential activation in subcortical features corresponding to the auditory brainstem pathways. Features A, D, and J include elements of the reticular auditory pathway of the brainstem which has been identified by electrophysiological studies to have a key role in auditory perception of location of sounds as well as the ability to filter a source of sound in background noise. Roughly these features appear to encompass key elements of the auditory pathway at the level of the pons (D) including the cochlear nucleus, trapezoid body, lateral lemniscus and superior olive on the right, (A) inferior colliculus, medial geniculate on the left and (J) thalamus bilaterally (Kretschmann and Weinrich, 1998). Although the resolution of the fMRI scans (4 \u00d7 4 \u00d7 5 mm) is not sufficient to resolve these structures individually, differences in activation in these regions, as indicated by reference to the higher resolution anatomical images, suggest that brain stem auditory nuclei may be involved.", "measurement_extractions": [ { "quantity": "4 \u00d7 4 \u00d7 5 mm", "unit": "mm", "measured_entity": "fMRI scans", "measured_property": "resolution" } ], "split": "test", "docId": "S2213158213001253-2583", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(C and E) Monkey MHC-I (Mafa) allele sequences detected by next-generation sequencing (C) and cluster analysis of the monkey MHCs (E). The colored letters indicate a comparatively high expression level of the MHC-I allele, comprising >10% of cDNA sequence reads. The gray background indicates the MHC-A allele, and the others indicate the MHC-B allele.", "measurement_extractions": [ { "quantity": ">10%", "unit": "%", "measured_entity": "cDNA sequence reads", "measured_property": "colored letters" } ], "split": "test", "docId": "S2213671113000738-447", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "Next, we investigated the expression of major histocompatibility complex class I (MHC-I) in iPSC-derived neural cells. Flow cytometry using antibodies against human leukocyte antigen (HLA)-A, HLA-B, and HLA-C revealed that mature neurons on days 35 and 71 expressed only a low level of MHC-I, and that the expression was enhanced in response to interferon gamma (IFN-\u03b3: 25 ng/ml for 48 hr; Figure 2A). The expression level of the mRNAs was approximately 1:100 compared with peripheral blood cells in both fibroblast- and blood-cell-derived iPSCs, which was again increased by exposure to IFN-\u03b3 (Figure 2B). These results suggest that donor-derived neurons could express MHC-I when INF-\u03b3 was secreted by the host brain under inflammatory conditions.", "measurement_extractions": [ { "quantity": "48 hr", "unit": "hr", "measured_entity": "iPSC-derived neural cells", "measured_property": "response to interferon gamma (IFN-\u03b3" }, { "quantity": "approximately 1:100", "unit": null, "measured_entity": "major histocompatibility complex class I (MHC-I) in iPSC-derived neural cells", "measured_property": "expression level of the mRNAs" }, { "quantity": "25 ng/ml", "unit": "ng/ml", "measured_entity": "interferon gamma (IFN-\u03b3", "measured_property": null } ], "split": "test", "docId": "S2213671113000738-667", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "For in vivo studies, the fixed frozen brains were sliced at 40 \u03bcm thickness and immunologically stained via the free-floating method. The primary antibodies used are listed in Table S3. See also Supplemental Experimental Procedures.", "measurement_extractions": [ { "quantity": "40 \u03bcm", "unit": "\u03bcm", "measured_entity": "fixed frozen brains were sliced", "measured_property": "thickness" } ], "split": "test", "docId": "S2213671113000738-787", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(F) After 25 days of differentiation, cells expressed hepatic markers (ALB, AFP, and A1AT). C, undifferentiated hESC; Hep, hFSC-derived hepatocytes; F, fetal liver; A, adult liver).", "measurement_extractions": [ { "quantity": "25 days", "unit": "days", "measured_entity": "cells", "measured_property": "differentiation" } ], "split": "test", "docId": "S2213671113000908-643", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "(B) IF analysis of day 8 SOX2-GFP AFE cultures (scale bar represents 200 \u03bcm).", "measurement_extractions": [ { "quantity": "200 \u03bcm", "unit": "\u03bcm", "measured_entity": "scale bar", "measured_property": null } ], "split": "test", "docId": "S2213671113000921-756", "dataset": "measeval" }, { "instruction": "\n You are an expert at extracting quantity, units and their related context from text. \n Given a paragraph below identify each quantity and its related unit and related context, i.e. the measured entity and measured property if they exist.\n ", "paragraph": "On first postoperative exam (days 3\u20135), a white retinal opacity overlying the hRPE implant was seen on fundus photography, along with occasional triamcinolone crystals trapped around the retinotomy site. The whitish opacity was subsequently lost in both hRPE implant types, with all remaining follow-up exams (beyond 1 week postoperation [post-OP]) showing a steep edge around the implant periphery (Figure 2A for fetal and 2C for adult; Movies S3 and S4).", "measurement_extractions": [ { "quantity": "beyond 1 week", "unit": "week", "measured_entity": "follow-up exams", "measured_property": null } ], "split": "test", "docId": "S2213671113001306-1385", "dataset": "measeval" } ]