dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonetheless, the data that was collected by the combined MLS and MBES herein struck a good balance between area of coverage, both above and below water, and suitably high-resolution data (for characterising the complex nature of the landforms) with the setup capable of collecting ~40 km of high spatial resolution (e. g. 0. 05 × 0. 05 m point spacing) river bank data in one survey day. The limiting factor in undertaking such long baseline surveys is the operating range of the radios transmitting and receiving the RTK corrections and the requirement to limit very long GPS baselines. The integrated MLS and hydro-acoustic (MBES and aDcp) survey approach used herein to characterise the complex banks and flow of the Mekong River offers considerable potential for advancing our understanding of fluvial process dynamics.
Acknowledgements-This research was supported by awards  NE/JO21970/1 ,  NE/JO21571/1  and  NE/JO21881/1  (to the  Universities of Southampton, Exeter and Hull , respectively) from the  UK Natural Environmental Research Council (NERC). We thank the  Mekong River Commission and Department for Hydrology and Water Resources in Cambodia  for logistical support. Award of a  University of Southampton Diamond Jubilee International Research Fellowship  to JLB helped facilitate completion of this paper. We thank the editor,  Stuart Lane , and two further anonymous reviewers for their thoughtful comments which improved an earlier version of this manuscript.
Results of the deepest all-sky survey for continuous gravitational waves on LIGO S6 data running on the Einstein@Home volunteer distributed computing project The LIGO Scientific Collaboration and The Virgo Collaboration We report results of a deep all-sky search for periodic gravitational waves from isolated neutron stars in data from the S6 LIGO science run. The search was possible thanks to the computing power provided by the volunteers of the Einstein@Home distributed computing project. We find no significant signal candidate and set the most stringent upper limits to date on the amplitude of gravitational wave signals from the target population. At the frequency of best strain sensitivity, between 170. 5 and 171 Hz we set a 90% confidence upper limit of 5. 5 × 10 -25 , while at the high end of our frequency range, around 505 Hz, we achieve upper limits 10 -24.
It should be noted that the Powerflux upper limits are set at 95% rather than 90% but refer to 0. 25 Hz bands rather than half-Hz. (i. e. gravitational wave spindown). This is shown in Fig. 10. We find that for most frequencies above 230 Hz our upper limits exclude compact objects with ellipticities of 10 -6 10 38 kg m 2 I (corresponding to GW spindowns between 10 -12 Hz/s and 10 -11 Hz/s) within 100 pc of Earth. Both the ellipticity and the distance ranges span absolutely plausible values and could not have been excluded with other measurements. We expect the methodology used in this search to serve as a template for the assessment of Einstein@Home run results in the future, for example the next Einstein@Home run, using advanced LIGO data that is being processed as this paper is written. Results of searches for continuous wave signals could also be mined further, probing sub-threshold candidates with a hierarchical series of follow-up searches.
This is not the topic of this paper and might be pursued in a forthcoming publication. This document has been assigned LIGO Laboratory document number LIGO-P1600156-v22. Appendix A: Tabular data    III : Instrumental lines identified and "cleaned" before the Einstein@Home runs. The different columns represent: (I) the source of the line; (II) the central frequency of the instrumental line; (III) the number of harmonics; (IV) Low-Frequency-Side (LFS) of the knockout band; (V) High-Frequency-Side (HFS) of the knockout band; (VI) the interferometer where the instrumental lines were identified. Note that when there are higher harmonics, the knockout band width remains constant. 190. 763 190. 813 D 192. 363 192. 613 D 193. 613 194. 313 D 196. 963 197. 013 D 197. 713 197. 763 D 197. 863 197. 963 D 198.
Previous models coupling fluxes and particles Models coupling fluxes and particles have been developed in other scientific fields, in particular in fluid mechanics, and are known as smoothed particle hydrodynamics (SPH) models  (Gingold and Monaghan, 1977). The philosophy of these models is to simplify fluids to a limited set of discrete fluid particles moving under a force field. The distribution of the particles at the next time step is then used to estimate the new field of quantities such as forces, temperatures, densities or water flow. In geomorphology, the original landscape evolution model introduced by  Mitasova et al. (2004)  in the GIS software GRASS belongs to this class of models. In addition, authors used moving grains to predict topographical variations on hillslopes and in rivers.
Acknowledgements. We thank the two anonymous reviewers for constructive reviews. This paper is a contribution to the  LMI CO-PEDIM  (funding from  IRD). S. Carretier  thanks the  Departamento de Geología of the Universidad de Chile  for its welcome. M. Reich acknowledges funding by the  MSI  grant " Millennium Nucleus for Metal Tracing Along Subduction " ( NC130065 ). Cidre sources are available upon request. The initial Cidre core was developed by  B. Poisson  at  BRGM. This paper is dedicated to her memory. Edited by:  S. Castelltort.
g. PREM). The LGFs are formed by spherical harmonic combinations of load Love numbers, which are derived from integrating the equations of motion through the layered Earth structure with normal-traction boundary conditions applied at the surface  (Farrell 1973). We evaluate eq. (  1 ) using software developed in-house, LoadDef, which is parallelized and written in Python. Initially, we compute the load Love numbers with respect to the centre of mass of the solid Earth, known as the CE reference frame  (Blewitt 2003). The GPS orbit and clock products from our observational analysis, however, are provided in the CM reference frame, which is referenced to the centre of mass of the entire Earth system (including the solid Earth, oceans, and atmosphere)  (Desai & Ray 2014). For investigations into Earth's OTL response, predictions and observations must be computed in the same reference frame; otherwise, positioning errors of order 1 mm or more may arise  (Fu et al.
01 • from θ = 1-10 • ; θ = 0. 1 • from θ = 10-90 • ; and θ = 1. 0 • beyond θ = 90 •. For each θ in the mesh, α = 0. 1 •. To also refine the integration mesh around the coastline, we first extrapolate the ocean model inland by one grid cell and then apply a land-sea mask. In the far-field, we define the land-sea mask using ETOPO1, which provides global topographic and bathymetric relief information at 1 arc-minute resolution  (Amante & Eakins 2009). In the near-field (within 1. 5 • of a station), we use the Global Self-consistent, Hierarchical, High-resolution Shoreline (GSHHS) database at full resolution  (Wessel & Smith 1996). Since ETOPO1 registers floating ice shelves as landmasses, but the tides remain active in those regions (Fig. 6 ), we use the Scientific Committee on Antarctic Research (SCAR) Antarctic Digital Database version 6.
0 to define the Antarctic coastline. The value for seawater density in eq. (  1 ) should represent the density of ocean water at the seafloor  (Ray 2013) ; here, we adopt a uniform value of 1035 kg m -3. LoadDef differs from most other OTL-response modelling software in that it derives LGFs on-the-fly from an input Earth model, rather than uses pre-computed LGFs from published tables. The convolution portion of LoadDef, however, is modelled after, and therefore not significantly different algorithmically from, the SPOTL package  (Agnew 2012 ) and both codes yield very similar results (to within about 1-2 per cent for SPOTL version 3. 3. 0. 2). Minor differences arise from details of the coastline refinement, resolution of the integration mesh, and values adopted for seawater density. In addition to an SNREI Earth model, the forward model (eq.
The figure also shows a corresponding set of predictions derived from PREM and FES2012 using our convolution software LoadDef. In general, the observations and predictions show great resemblance for each harmonic, including the small-amplitude M f harmonic. One notable discrepancy between observations and predictions, however, is the apparent northward offset of the vertical-displacement response for the M f harmonic seen in the mid-continent (Fig. 11 , panels e and f, 30 • S-10 • S). In particular, the smallest response amplitudes for the vertical component occur further to the north in the observational results (panel e) relative to the predicted results (panel f). Since the observations exhibit spatial coherency, the offset is unlikely to result solely from random observational error. Uncertainties in the ocean-tide models, which are difficult to constrain for the small-amplitude long-period tides, might account for some of the spatial discrepancy.
For thirteen stations that recorded at least 10 yr of data, we computed statistics on the distributions of harmonic coefficients derived from the yearly analyses. The 2σ standard deviations for the amplitudes and phases are listed in Table  4. Although the estimates may seem relatively high, in this case we have only performed the harmonic analyses on up to 1 yr of data at a time, rather than on multiple years of data. Performing the power-spectrum analysis (Method 1) on a single year of data also shows similarly larger errors (Table  4 , centre columns) and provides an additional level of confidence in the error estimates. Moreover, the estimates of observational uncertainty are generally consistent with the 0. 2 mm level uncertainties derived by  Penna et al. (2015)  through rigorous synthetic testing.
The accuracy of the harmonic estimates generally improves with longer time spans of data. Since good convergence for the lunarderived harmonics may be achieved with at least ∼1000 d of data  (Yuan et al. 2013 ) and the median time-series length for our station   12 , but for the M f tidal harmonic. Note, however, that the scales for the ellipses and ocean-tide amplitude differ from Fig. 12. The GOT4. 10 model does not include M f ; therefore, panel (f) shows the residuals between the observed and predicted OTL-induced surface displacements computed using AK135f and FES2012. Table  3. RMS misfits between observed and predicted OTL-induced surface displacements (from Figs  16  and 17 ). Solid Earth body tides were removed according to IERS conventions at the GPS processing stage. A common-mode component (network-mean OTL-induced displacement) was also removed prior to the RMS computation.
Even the Earth model designed to represent stable continental shield structure, SNA, does not generate significantly better predictions of the OTL-induced surface displacements for South America than the globally averaged models. Other contributors to the misfit include observational uncertainties (e. g. GPS data acquisition, GPS data processing, harmonic analysis) and deficiencies in the forward model (e. g. numerical integration of eq. 1, spatial variations in seawater density, deviations from SNREI structure). We estimated the observational error using two different techniques, which yielded similar results (Table  4 ). Processing multiple years of data in a single inversion clearly has the potential to improve the accuracy of observed OTL-induced surface displacements, evidenced by the reduction of error for analyses of several years of data compared to analyses of one year of data.
In particular, for thirteen stations with long data records (>10 yr), we estimate that the horizontal-displacement errors for the M 2 and O 1 tidal harmonics are, remarkably, less than 0. 1 mm at two standard deviations, which rivals the sensitivity to choice of ocean-tide model and SNREI Earth structure for many of the stations in the South America network. In general, the residuals derived for the M 2 tidal harmonic in South America significantly outweigh the two-sigma observational uncertainties (Fig. 15 ), leaving open the possibility to invert for structural deficiencies at that frequency. Furthermore, the spatiotemporal characteristics of the M 2 residuals remain regionally coherent as well as consistent between forward models (Fig. 12 ), indicating that all of the RMS misfit cannot apparently be explained by errors in the observations and adopted forward models.
S U M M A RY A N D C O N C L U S I O N S We derived OTL-induced surface displacements from sub-daily GPS time-series using kinematic PPP and harmonic analysis for a network of stations in Brazil, Argentina, and Uruguay. After a common-mode component representing the network-mean OTL response is removed from each station, the misfits between the observed and predicted OTL-induced displacements are approximately 0. 1-0. 3 mm for the dominant tidal harmonics in three distinct frequency bands: M 2 , O 1 and M f. An assessment of the observational error suggests that, at least for the M 2 harmonic and sufficiently long time-series, the OTL-response residuals exceed random noise from the data processing. Therefore, OTL-response residuals may potentially be used to refine ocean-tide models and to constrain solid-Earth structure.
We postulate that part of the spatial coherency could be attributed to deficiencies in the a priori Earth model, which would undoubtedly include deviations from an assumed SNREI structure (e. g. anelasticity, anisotropy, and lateral heterogeneities). As ocean-tide models, OTL-response modelling, and data processing methods continue to improve, the ability to probe Earth structure through observations of OTL-induced surface displacements becomes increasingly tractable.
A C K N O W L E D G E M E N T S We are indebted to Duncan Agnew, Richard Ray, Matt King and an anonymous reviewer for insightful ideas and valuable critiques that greatly improved our manuscript. We also sincerely thank Shailen Desai for helpful discussions on OTL analysis and tidal geocentre variations as well as Angelyn Moore and Willy Bertiger for providing ongoing GIPSY support. Dan Bower graciously supplied scripts to extract and plot the seismic tomography data shown in Supporting Infirmation Fig. S5. The GPS data used in our study was made available by the governments of Brazil (Instituto Brasileiro de Geografia e Estatística), Argentina (Instituto Geográfico Nacional), and Uruguay (Servicio Geográphico Militar). We used geographic information from the Scientific Committee on Antarctic Research (SCAR) Antarctic Digital Database (ADD) to develop a land-sea mask around Antarctica.
We gratefully acknowledge support from the National Science Foundation Geophysics Program funding under Grant No. EAR-1417245. This manuscript is based upon work supported by the NASA Earth and Space Science Fellowship to HRM under Grant No. NNX14AO04H. Some figures were generated using Generic Mapping Tools  (Wessel et al. 2013). Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Please note: Oxford University Press is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the paper.
e. the predicted OTL response at the master site). All synthetic tests used up to one year of data from 2010 at each of the six test stations in South America. To guide our exploration of coordinate process noise values to test, we first computed a range of theoretically suitable values. We estimated the instantaneous velocity, v inst , experienced by any given station as: v inst = d dt (A cos ωt) = -A ω sin ωt, ( A1 ) where A is the signal amplitude, ω is the frequency of the signal, and t is time. The absolute value of the instantaneous velocity given by eq. (  A1 ) is maximized when the quantity sin ωt = ±1, which occurs at the maximum slope of the harmonic wave. For the M 2 tidal harmonic, ω = 0. 5059 rad hr -1 and the response amplitudes in South America reach as high as ∼4 cm. Thus, the maximum instantaneous velocity, v max , expected for our network is ∼20 mm hr -1.
2. 1. Oxygen isotopic composition of environmental water (δ 18 O W ) in Trondheim and in the surrounding regions Since the isotopic composition of tooth enamel carbonate derives from environmental water, this study focuses on identifying both spatial (Central Norway and surrounding areas) and temporal (from medieval times to present) variation. The oxygen isotopic composition in precipitation in Norway is not very well established, and except for the GNIP (Global Networks of Isotopes in Precipitation) station at Lista on the south coast, there is no available data for mainland Norway. The only available information for Trondheim comes from different modulations (GNIP, isomap. org and OIPC). Modulations will, however, never be as accurate as local water analyses. Moreover, local measured waters should be preferred to estimations for calibrating the present day value of enamel δ 18 O  (Daux et al.
Each sample was taken as a slice from the side of the tooth; no samples were taken from the occlusal surface. The enamel was sonicated and ground to a fine powder. An aliquot was weighted out and kept in a 120 °C oven for 24 h. The oxygen isotopic analyses were performed at the GNS Science's stable Isotope Laboratory. No pre-treatment of the enamel was carried out. CO 2 was obtained by acid hydrolysis using phosphoric acid at 50 °C and collected by cryogenic distillation. Isotopic ratios were measured on a dual inlet ratio GVI Isoprime mass spectrometer. One must note that our data may not be fully comparable to values measured on enamel samples which have been subjected to pre-treatments intended to remove any secondary carbonates  (Koch et al. , 1997; Grimes and Pellegrini, 2013). All results are reported as per mil (‰) normalised to the PDB scale using an in-house carbonate reference material (GNS Marble; reported value of -6.
Present day composition of environmental water The monthly values of the δ 18 O W of precipitation sampled in Trondheim in 2013 are presented in Table  1. The data for January was estimated because the measured value, equal to -19. 86‰, was not plausible. Indeed, such a value, corresponds to Arctic environments (for instance, values as low as this one are measured, although seldom, further north in NY-Alesund in Svalbard, Norway; GNIP data). The extreme depletion of this sample may be attributable to the small amount of precipitation this month (44 mm), which was composed mainly of two days of snowfall (Bjørn Frengstad, pers. com. ), and is not likely to represent a complete sample. The yearly weighted δ 18 O W of the precipitation in Trondheim in 2013 is -8. 77‰. The oxygen isotopic composition of the Trondheim tap waters sampled in October 2014 and September 2015 are -8.
Migration during childhood As stated in Section 2. 3. , the oxygen isotopic compositions of the M1 and M3 teeth of an individual can be different because of the change of beverage from milk to water during early childhood. This effect can induce a bias in the interpretation of isotopic differences between M1 and M3 in terms of migration. We therefore sought to quantify and eliminate the breastfeeding effect from the Trondheim teeth database prior to interpreting the differences between the M1 and M3 teeth compositions. We then evaluated if the isotopic composition of the drinking water, ingested by nursing mothers or by the individuals themselves, could vary in response to differences in mean temperatures during the times of formation of the two teeth. As already mentioned, the mean enrichment of M1 teeth with respect to M3 is generally b 1‰.
Moreover, within a population the inter-individual variability of the enrichment is frequently several tenths of a per mil  (Evans et al. , 2006b; White et al. , 2005; Wright and Schwarcz, 1999; Wright and Schwarcz, 1998). To take this possible variability into account, we have calculated adjustments of the isotopic composition of the M1 of the present database to compensate for offsets ranging from 0. 2‰ to 1‰. The M1 and M3 are formed at two different periods of life (0-3 and ~8-14 years), during which the mean temperature may be different. We tested this assumption using the Trondheim temperature series provided by CRUTEM4 for the period 1970-2014. Using  Dansgaard's (1964)  equation, we transformed it into a δ 18 O W series with which we calculated the 10-year running mean (δ 18 O W-10 ) to take into account a 10-year residence time in the reservoir.
Acknowledgments This research would not have been possible without the support of the  Research Council of Norway  through grant number:  220821. We would also like to acknowledge the contribution of the following people:  Bjørn Frengstad  at  NGU  for setting up the precipitation collector and collecting the monthly water samples;  Jon Anders Risvåg  og  Birgitte Skar  at the  NTNU Museum of Science  for granting us access to the skeletal material; and  Charles Utvik  for his invaluable effort while collecting the enamel samples together with the author (S. S. H. );  Nina Sivertsen  at  Tanngarden dental clinic in Trondheim  for providing modern tooth samples from Trondheim. We would also like to thank  Darren G. Mann  and  Helen Frances Leslie-Jacobsen  for proofreading the article.
The representation of land surface processes and fluxes in climate models critically affects the simulation of near-surface climate over land. Here we present an evaluation of COSMO-CLM 2 , a model which couples the COSMO-CLM Regional Climate Model to the Community Land Model (CLM4. 0). CLM4. 0 provides a more detailed representation of land processes compared to the native land surface scheme in COSMO-CLM. We perform historical reanalysis-driven simulations over Europe with COSMO-CLM 2 following the EURO-CORDEX intercomparison protocol. We then evaluate simulations performed with COSMO-CLM 2 , the standard COSMO-CLM and other EURO-CORDEX RCMs against various observational datasets of temperature, precipitation and surface fluxes. Overall, the results indicate that COSMO-CLM 2 outperforms both the standard COSMO-CLM and the other EURO-CORDEX models in simulating sensible, latent and surface radiative fluxes as well as 2-meter temperature across different seasons and regions.
0 and its input datasets is provided in  Oleson et al (2010). In the present study, CLM4. 0 is used in its biogeophysics-only configuration without carbon and nitrogen dynamics. The only modification we included to CLM4. 0 concerns two hydrological parameters influencing surface and subsurface runoff (i. e. , exponential decay factor influencing the saturation excess component of surface runoff and maximum subsurface drainage). Namely, we reverted to the values used in CLM3. 5 for these parameters (Lawrence et al 2011) since preliminary tests indicated slightly more realistic evapotranspiration rates over Europe for this parameter choice. The simulation performed with COSMO-CLM 2 follows the EURO-CORDEX protocol and is a sister simulation of the one performed with COSMO-CLM (table  1 ). That is, the same model version and model parameter set are used for the atmospheric component, the only difference being the LSM used.
Evaluation datasets The various reference products used for evaluation are described in table 2. The selected products cover temperature, precipitation and surface heat and radiation fluxes. When possible, different products are considered for a given variable in order to account for uncertainties in observation-based datasets. All the products are used at a monthly resolution and were regridded, using bilinear interpolation, to a common half-degree regular grid for comparison with the EURO-CORDEX models. For products not covering the full 1990-2008 EURO-CORDEX common analysis period a shorter time period is used instead.