dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Stephen Thompson  and  Rosemary Collier  ( States of Jersey Planning and Environment Department, Environment Division, Howard Davis Farm ) for their help with the collection of isolates on Jersey and  Clarisse Galet  ( Terres de Saint-Malo ,  35  St M eloir les Ondes),  Jean-Jo Habasque  (Syntec, 22 Pleumeur-Gautier),  Roland Pell  e (INRA, 29 Ploudaniel), and  Bruno Pitrel  ( SILEBAN, 50 Gatteville le Phare ) for their help with collecting isolates in Brittany and Normandy. We express our thanks to  Elodie Lesn  e,  S ebastien Epinette ,  Herv e Douchy , and  Gladys Mialdea  for their technical assistance in the biological tests. We are most grateful to  David E. L. Cooke  and  Alison K. Lees  ( James Hutton Institute, Dundee, Scotland ) for gift of control isolates for genotyping.
[9] ) and the passive cooling system, the science instrument and the Fine Guidance Sensor (FGS, described in Section 5. 8). The FGS was designed to be physically part of the Instrument Optical Bench (IOB) as it requires a good coalignment with the science channels. In addition this allows maximum synergies between the Visible-Near InfraRed (VNIR) channel and the FGS (e. g. detectors, electronics etc. ) and was the preferred design since the FGS would also have been used to extract science data for on-ground post-processing of the science instrument data and visible photometry. The FGS was designed to interface with the S/C AOCS subsystem as an integral part of the AOCS control loop.
Fine guidance system The main task allocated to the FGS was to ensure the centering and guiding of the satellite using the visible/NIR light from the target star to determine changes in the satellite pointing and feed these back to the AOCS in a closed loop system. The use of a co-aligned sensor was shown to be an important contributor for the AOCS RPE performance in terms of the achievable single-star centroiding accuracy. The FGS was also to be used to ensure the system was in focus during the initial verification phase of the mission. The final function of the FGS was to provide high precision astrometry and photometry of the target for complementary science. In particular, the data from the FGS was to be used for de-trending to aid in the data analysis on ground. The FGS optical module was designed for a 20 arcsec square field of view using 50 % of the flux of the target star below 1.
0 μm wavelength. The optical module provides for internal cold redundancy of the detector chain through the use of an internal 50/50 beam-splitter and two independent detector channels with their own cold and warm drive electronics with a common Gregorian telescope feeding both detectors. A small de-focus offset on each detector (in opposite directions) was designed to allow the FGS to be used during spacecraft ground testing and commissioning as a coarse wave-front sensor (Shack-Hartmann interferometer) for the telescope. The baseline optical module design is shown in Fig. 7. A dedicated FGS Control Electronics (FCE) unit was designed to provide the control and processing of the FGS data and pass centroid information to the S/C AOCS. The assessment of the FGS accuracy for the faintest target goal star defined for EChO (considering the effective collecting area of the telescope, efficiency parameters of optical elements, beam splitter and QE of the detector) showed that the photo-electron count would be more than 10 4 per second.
Predicted instrument performance In the past, general-purpose, space-based instruments used for exoplanet atmosphere characterisation have suffered from a high level of systematic error. EChO was designed to be an instrument that performed time series spectroscopy with unprecedented photometric stability simultaneously from the visible to the mid-IR. The performance of the mission is governed by a number of different noise sources for which the radiometric noise contributions have been determined using a sophisticated software simulation (EChOSimsee  [20] ) for a number of different conditions corresponding to the faintest and brightest target required to be observable with EChO. Using the EChOSim tool, we can evaluate the performance by evaluating the overall noise allocation and comparing this to the requirements laid out in the EChO Science Requirements and SRE-PA/2011.
The simultaneous measurement of a wide wavelength range ensures that any correlated noise sources can be tracked and accounted for in the data processing. This allows, for instance, the effects of stellar variability to be accounted for and minimised in the data processing as well as any other sources of correlated systematic noise. A comprehensive simulation of the performance of the EChO mission design has been performed which shows that the system would have been limited only by the photon noise from the star for all the targets in the EChO core survey. EChO was not selected for the ESA M3 slot, however the design study has demonstrated that a similar mission is viable and provides a realistic option for furthering exoplanet research. Further progress in detector performance and the reliability of cryogenic system for space applications will mean any future mission will have even better capabilities.
Observations Over the last few decades, the number of surface pCO 2 measurements and their spatial and temporal coverage has increased substantially. The main reasons for this are the technological advancements and the continuous efforts in the installation of autonomous underway CO 2 instruments  (Pierrot et al. , 2009; Watson et al. , 2009)  onto volunteer observing ships (VOS), primarily commercial ships. The underway CO 2 observations have recently been compiled into a single database called the Surface Ocean CO 2 Atlas (SOCAT,  Pfeil et al. , 2013; Sabine et al. , 2013). With the release of SOCAT version 2 (http://www. socat. info, Bakker et al. , 2014) , the total number of publicly available pCO 2 measurements is more than 10 million originating from a total of 2660 cruises between 1968 and 2011.
Data prior to the year 1970 were excluded from our analysis because of the limited number of observations. Therefore, the temporal coverage of the data sets used in this study is from 1 January 1970 to 31 December 2011. We excluded coastal measurements in SOCATv2, defined here as those with bottom-depth shallower than 500 m. We used bottom-depth information from ETOPO2 (http://www. ngdc. noaa. gov/mgg/global/etopo2. html) included in SOCATv2. We also removed all SOCATv2 data that are well beyond realistic range (i. e. exceeding two standard deviations from the database mean), potentially due to additional coastal influences and mesoscale variability. In addition to pCO 2 measurements, we compiled the publicly available merged global bottle data sets into one collection. This consists of data from the CARINA (CARbon dioxide IN the Atlantic Ocean,  Key et al.
, 2003). Here, we used discrete measurements (bottles) from depths more shallow than 5 and 10 m for BATS and HOT, respectively. These data were downloaded from (http://bats. bios. edu/ bats_form_bottle. html) and (http://hahana. soest. hawaii. edu/hot/hot-dogs/bextraction. html). The publicly available surface pCO 2 data from the eastern subpolar North Pacific region and Station P  (Wong and Johannessen, 2010)  are also included in the observational collection (downloaded from http://cdiac. ornl. gov/ftp/oceans/station_p_ca/). The carbon data were averaged into monthly means over 48)48 regular bins. The 48 )48 grid size was selected because it gives a reasonable temporal coverage and yet is still able to represent the large-scale spatial patterns of variability, while reducing the impact of mesoscale variability.
5 scenario or are even somewhat higher  (Peters et al. , 2012). In both historical and RCP8. 5 simulations, all models use the same prescribed atmospheric CO 2 concentrations, aerosols, and other greenhouse gases (e. g. CFCs and CH 4 ) as outlined in  Taylor et al. (2012). We derived the monthly surface ocean pCO 2 model fields from the prognostic monthly ALK, DIC, SST, and SSS fields using the carbon chemistry formulation as described in the previous subsection. This allows a more consistent interpretation of the model output when we later investigate the effects of the individual controlling factors (i. e. SST, SSS, DIC, and ALK) for the surface pCO 2 trend. To ensure that the model outputs were consistent with the observations, we also interpolated them onto the same 48 )48 regular grid using uniform weighting for all data within each grid cell.
Decomposition of pCO 2 trend Here, we assume that the actual pCO 2 trend =dt is an indicator for the pCO 2 trend resulting from the long-term change in SST. The same approach was applied for the other three parameters (i. e. SSS, DIC, and ALK). Our pCO 2 trend decomposition is useful to identify the main drivers regulating the trend, and provides insight on the controlling mechanisms acting in different regions. To do this requires good temporal coverage of SST, SSS, DIC, and ALK, which is generally lacking in the observational data set. For this reason, the decomposition was only applied to the model data and the available long-term observations at BATS and HOT stations. In case only underway pCO 2 observations were available at these locations, ALK values were filled with those computed using  Lee et al.
Discussion Surface ocean pCO 2 is a directly measurable biogeochemical parameter that can be used as an indicator of long-term climate change. It provides information on the ocean carbon sources and sinks as well as on any potential climate feedback due to changes in ocean biogeochemistry. In this study, we estimated the regional pCO 2 trends over the last four decades based on the latest data collection of directly and indirectly measured surface pCO 2. Model outputs were also applied to estimate pCO 2 trends in the contemporary and future periods.
The good agreement between the observed and subsampled model trends is encouraging. Assimilation of the growing surface pCO 2 database into ocean biogeochemical general circulation models should be explored in order to obtain the best estimate of ocean carbon sinks from both models and data. Data assimilation will also be useful to identify model deficiencies as well as to evaluate the quality of the current observational networks. Largely due to the extensive sampling programs in the North Atlantic and eastern mid-latitude North Pacific regions, the long-term feedback monitoring strategy of surface pCO 2 carried out in these regions over the last decades has been successful (e. g. Schuster and Watson, 2007; Olsen et al. , 2008; Watson et al. , 2009; Metzl et al. , 2010; Nakaoka et al. , 2013).
Acknowledgements We thank three anonymous reviewers for their critical feedback, which helped to improve the manuscript significantly. The research leading to these results was supported by the  Centre for Climate Dynamics at the Bjerknes Centre  and the  EU   FP7  project  CARBOCHANGE 'Changes in carbon uptake and emissions by oceans in a changing climate ', which received funding from the  European Commission 's  Seventh Framework Programme  under grant agreement no. 264879. SOCAT, GLODAP, CARINA, and PACIFICA are international efforts supported by the  International Ocean Carbon Coordination Project (IOCCP). The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to the above data products. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP5.
Observations The pilot region covers the range 334 • < l < 344 • in Galactic longitude and |b| < 2 • in Galactic latitude. Observations were made between 2012 May and November with the Australia Telescope National Facility (ATNF) Parkes 64m telescope. The receiver was the H-OH receiver, with a standard feed that provided two orthogonal linear polarisations. Data were taken in on-the-fly (OTF) mapping mode, in which spectra are taken continuously as the telescope is scanned across the sky. The survey region was divided into 2 × 2 degree tiles, each of which was mapped a total of ten times to achieve target sensitivity. Repeat maps were scanned alternately in the Galactic latitude and longitude directions to minimise scanning artefacts. The scan rate was 34 arcsec s - 1  , with data output every 4 s at intervals of 2.
Typical system temperatures were 25-30 K, excluding pointings towards bright continuum sources within the Galactic Plane. Data were recorded simultaneously in three frequency bands centred on 1612, 1666 and 1720 MHz, each with 8192 spectral channels over a bandwidth of 8 MHz. This corresponds to a velocity coverage and channel width of ∼ 1400 km s -1 and 0. 18 km s -1 respectively.
Data reduction Bandpass calibration, flux density calibration and velocity scaling was carried out with the ASAP 1 and Livedata 2  data reduction packages, in combination with python modules currently under development as part of the SPLASH data reduction pipeline. Bandpass calibration was performed using off-source reference spectra according to: S * = PON -POFF POFF Tsys,OFF (1) where S * is the flux density of the source position, PON and POFF are the on-source and off-source power measured by the telescope, and Tsys,OFF is the system temperature at the off-source position (in Jy). Residual spectral baseline structure was removed by flagging bright spectral lines, performing a linear interpolation over the missing channels, then heavily smoothing the remaining spectrum with a Gaussian kernel of σ = 220 channels (∼ 40 km s -1 ) to produce a model of the continuum flux and residual baseline shape.
30, 1. 34 and 1. 39 Jy K -1 for 1612, 1666 and 1720 MHz 3. From hereon any reference to observed brightness temperature is referring to this main-beam temperature. Gridding of the spectra into spatio-velocity cubes was performed with the Gridzilla 2 software package  (Barnes et al. 2001). We use a Gaussian smoothing kernel of 20 truncated at a cutoff radius of 10 , which results in an effective resolution (FWHM) of ∼ 15. 5 arcmin. This relatively large kernel size is chosen to improve the sensitivity to extended sources and optimise the spectral line cubes for weak, extended signal. The statistic used to compute the brightness temperature in a given pixel is a weighted mean with censoring of outliers, chosen for its robustness against RFI. The two orthogonal polarisations are summed during gridding to produce total intensity (Stokes I) cubes.
A separate set of Jy-scaled, point-source-optimised cubes were created for maser science applications, by enabling the 'beam normalisation' option in Gridzilla. Further details of the gridding algorithm can be found in  Barnes et al. (2001)  and the Gridzilla in-package documentation. Post-gridding, low-level signal was discovered in two reference positions belonging to both the positive and negative latitude maps at 336 • < l < 338 •. This was corrected to the first order by removing the affected velocity range (-5. 5 < vLSR < -2. 5 km s -1 ) from the raw reference spectra and linearly interpolating over the missing channels. The residual effect on the data is small, and has no significant effect on the analysis in this work. Accurate fitting and subtraction of reference position signal will be carried out in the final processing pipeline.
In order to recover the absolute value, we must therefore obtain an estimate of the brightness temperature at 1. 6-1. 7 GHz at the survey reference positions. For this we make use of the S-PASS 2. 3 GHz  (Carretti et al. 2013)  and HIPASS 1. 4 GHz  (Calabretta et al. 2013 ) continuum surveys of the Southern sky. The HIPASS data use a full beam temperature scale, are absolutely calibrated, and include the 2. 73 K cosmic microwave background (CMB). S-PASS data includes no CMB contribution and no zero-level offset, and has an uncertainty in its zero level of 0. 1 K  (Carretti, private communication). Both datasets have an effective beam size of 14. 5 arcmin (S-PASS was originally 10. 75 arcmin, smoothed here to the HIPASS resolution for the goals of this work). The SPLASH reference positions are located at |b| > 2.
25 • , where the continuum emission is primarily diffuse and extended compared to the Parkes beam. In order to compute a spectral index at each reference position, we first subtract the zero level offset of 3. 3 K from the HIPASS data (Cal- abretta et al. 2013), where this value contains the 2. 73 K CMB and a ∼ 0. 6 K contribution from the Galactic diffuse synchrotron emission in the lowest emission areas. The map is then rescaled to a main beam temperature scale assuming a main beam efficiency of η mb = 0. 60 ± 0. 08. This value and its uncertainties are estimated from the efficiency implied by the scaling of  Calabretta et al. (2013,  η mb = 0. 52), and that measured for the central beam of the Parkes multibeam receiver  (Staveley-Smith et al. 1996 , η mb = 0. 69). Spectral indices at each reference position were then computed from the HIPASS and S-PASS data, and used to estimate the continuum brightness temperature at 1612, 1666 and 1720 MHz respectively.
RESULTS OH is widely detected in all four transitions throughout the pilot region. Fig. 2  shows combined peak emission and peak absorption maps for each line, created by plotting the most extreme value of the brightness temperature, T b at each spatial position. Continuum contours and the peak positions of maser sources are also shown. Fig. A1  shows channel maps of the same region, in which the data have been binned in the velocity dimension to a channel width of 3. 6 km s -1 in order to improve the signal-to-noise. The main lines are dominated by a band of strong absorption along the Galactic Plane, much of which arises from the Scutum-Crux Arm at -50 vLSR -30 km s -1. The most prominent main-line emission is seen at velocities more negative than -120 km s -1 , consistent with a location in the 3 kpc arm (see e.
OH vs CO OH has been found to be an effective tracer of diffuse molecular gas outside the central, CO-bright regions of molecular clouds  (Wannier et al. 1993; Liszt & Lucas 1996; Barriault et al. 2010; Allen et al. 2012; Cotten et al. 2012) , as well as of cold self-absorbing atomic gas in primarily molecular regions  (Li & Goldsmith 2003; Goldsmith & Li 2005). Given this, we might expect the OH distribution in the Galactic Plane to be more extended than that of CO. However, in the SPLASH pilot region this is not observed. The channel maps in Fig. A1  show 12 CO(J=1-0) from the NANTEN Galactic Plane Survey  (Mizuno & Fukui 2004)  together with the four OH lines. The NANTEN data has been smoothed with a Gaussian kernel of FWHM=15 arcmin to match the resolution of the SPLASH cubes, and both datasets are binned to a velocity resolution of 3.
In regions which have not been subject to untargetted surveys the fraction of newly detected masers is expected to be greater. In Fig. 7  we show the distribution of peak flux densities for our and Caswell's data. For those masers detected in both observations, there is a reasonably good one-to-one correspondence in the peak flux densities, with some scatter. This suggests that the 1665-MHz masers typically vary by a factor of a few over the timescale between the observations for the two datasets (16-20 years). However, three non-detections in the Caswell data have substantial peak flux densities in our data (between 3. 8 and 7. 0 Jy). The most likely explanation for this is that these three objects have exhibited particularly strong flux density variations. We note the appearance of a significant asymmetry in the sources detected on a single epoch: 12 sources detected by SPLASH and not Caswell, compared to 1 detected by Caswell and not by SPLASH.
The larger number of sources detected only in SPLASH may be largely accounted for by the slightly less sensitive Caswell data, especially given the non-uniform sampling of the Galactic plane (8 of these 12 sources have flux densities significantly less than 1 Jy). Sevenster et al. (1997b)  surveyed the entire SPLASH pilot region for masers in the 1612-MHz line. They identi-   SPLASH and Caswell (1998). The dotted line represents equality between the two flux density measurements. Non-detections Caswell's data are plotted at his 5σ detection limit of 160 mJy, with arrows pointing left. The single source reported by Caswell that is not detected in the SPLASH data is plotted at a 5σ detection limit of 400 mJy (higher than most of the survey region due to the presence of bright continuum at that position) with an arrow pointing downwards.
fied 65 masers within this region, all but two of which are detected in our observations. Here we require the peak position of the maser in the Sevenster catalogue to fall either on or adjacent to the peak pixel position of the SPLASH source, and that the velocities ranges of the two sources overlap. Of the 61 detected sources, 59 appear as distinct sites within the Parkes beam. In addition to this, we detect 91 maser sources that were not detected in Sevenster's work. A comparison of the distribution of peak flux densities between the two datasets (Fig. 8 ) shows a scatter that is partly due to intrinsic variability. Generally, the points fall to the left of the dotted line of equality in the figure. This is due to a lower reported peak flux density in the Sevenster et al. data, arising from a spectral channel width that is considerably larger than that of the present work (1.
ACKNOWLEDGMENTS We wish to thank  Stacy Mader ,  John Reynolds ,  Brett Preisig ,  Mal Smith  and the entire Parkes telescope staff for their support and advice during the substantial hours of SPLASH project observing. We would also like to thank the referee,  Paul Goldsmith , for his prompt and helpful report. The Parkes Telescope is part of the  Australia Telescope  which is funded by the  Commonwealth of Australia  for operation as a National Facility managed by  CSIRO. The  NANTEN  project was based on a mutual agreement between  Nagoya University  and the  Carnegie Institute of Washington , and its operation was made possible thanks to contributions from many companies and members of the Japanese public. SLB acknowledges support from the  Australian Research Council. JFG acknowledges support from grant  AYA2011-30228-C03-01  of  MINECO  (which includes FEDER funds).
We present a simple, data-driven approach to generation from knowledge bases (KB). A key feature of this approach is that grammar induction is driven by the extended domain of locality principle of TAG (Tree Adjoining Grammar); and that it takes into account both syntactic and semantic information. The resulting extracted TAG includes a unification based semantics and can be used by an existing surface realiser to generate sentences from KB data. Experimental evaluation on the KBGen data shows that our model outperforms a data-driven generate-and-rank approach based on an automatically induced probabilistic grammar; and is comparable with a handcrafted symbolic approach.
As discussed in  (Power and Third, 2010) , one important limitation of these approaches is that they assume a simple deterministic mapping between knowledge representation languages and some controlled natural language (CNL). Specifically, the assumption is that each atomic term (individual, class, property) maps to a word and each axiom maps to a sentence. As a result, the verbalisation of larger ontology parts can produce very unnatural text such as, Every cat is an animal. Every dog is an animal. Every horse is an animal. Every rabbit is an animal. More generally, the CNL based approaches to ontology verbalisation generate clauses (one per axiom) rather than complex sentences and thus cannot adequately handle the verbalisation of more complex input such as the KBGen data where the KB input often requires the generation of a complex sentence rather than a sequence of base clauses.