dataesr/scientific-paragraphs-categorization huggingface dataset:
This study aimed to estimate the volume of freshwater and the relative contribution of each coastal basin flowing into Saint Brieuc Bay. Discharge was estimated at the mouth of each river, where no monitoring station is available. In this manner, the study aimed to estimate the discharge for 21 truly ungauged basins. This study also took advantage of seven monitoring stations located upstream from the four main basins in the bay. Streamflow discharge measurements were extracted from the French Banque HYDRO database (www. hydro. eaufrance. fr) at a variable time step and converted into a one-hour time step. Simulations were performed at an hourly time step.
, 2016). For each pixel of the digital elevation model (DEM), the flow path to the outlet is performed and the hydraulic length L corresponds to the length of the channelized part of the path within the river network only. This was estimated using GRASS 6. 4 supplemented with the toolkit of  Jasiewicz and Metz (2011). The DEM had a 50 m resolution but was resampled at 25 m using bicubic sampling to increase the number of pixels within small basins. The D8 algorithm (e. g. , O'Callaghan and  Mark , 1984)  was used to model drainage, and a predefined river network provided by the Sandre/CARTHAGE database was burned into the DEM using an algorithm based on the inverse distance to the network  (Nagel et al. , 2011). This procedure calculates the probability density function of the hydraulic length pdf (L) for each gauged and ungauged basin  (Aouissi et al.
Low variability provides a high degree of confidence that the ungauged basin is similar. Even though uncertainty is not assessed in a fully robust manner, because of the structural lack of data associated to the considered PUB approach, it partially solves the difficult choice of donor basin by taking advantage of all available information. Finally, it enables better graphical evaluation of simulations. One challenge of the approach remains on combining the time series of different donor basins in order to provide a robust average final simulation. For this study the donors have been combined on a case-by-case basis using simple average of the net rainfall time series of donor basins to estimate the one at the target ungauged location. However, when applied to a larger dataset, the approach could benefit from a quantification of the hydrological similarity between the donor and the target basins (e.
8. 219)  [37]  program (PYTHIA), with the CUETP8M2T4 tune  [36, 38, 39] , for parton showering and hadronisation. In order to compare the predictive powers of alternative ME, parton shower, and hadronisation models, two additional samples are generated using different generator setups. Firstly, a sample is generated using the MADGRAPH5 aMC@NLO  [40]  (v. 2. 2. 2) (MG5 aMC@NLO) generator including up to two extra partons at the ME level with NLO precision. In this setup, referred to as "MG5 aMC@NLO+PYTHIA[FXFX] ", MADSPIN  [41]  is used to model the decays of the top quarks, while preserving their spin correlation, and events are matched to PYTHIA for parton showering and hadronisation using the FxFx prescription  [42]. Secondly, a sample is generated with POWHEG at NLO in QCD at the ME level and interfaced with HERWIG++ (v.
This corresponds to next-to-NLO (NNLO) for W+jets and Z+jets  [49] , approximate NNLO for single top quark in the tW channel  [50] , and NLO calculations for diboson  [51]  and tt+Z/W  [52]. The tt predictions are normalised to a cross section of 832 +20  -29 (scale) ± 35 (PDF + α S ) pb calculated with the TOP++2. 0 program  [53]  at NNLO including resummation of next-to-next-to-leadinglogarithmic (NNLL) soft-gluon terms, assuming a top quark mass m t = 172. 5 GeV. Additional proton-proton interactions within the same or nearby bunch crossings (pileup) is simulated for all samples. The interactions of particles with the CMS detector is simulated using GEANT4 (v. 9. 4)  [54].
4  [58, 59]. Jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found from simulation to be within 5 to 10% of the true momentum over the whole p T spectrum and detector acceptance. Pileup can contribute additional tracks and calorimetric energy deposits to the jet momentum. To mitigate this effect, tracks identified to be originating from pileup vertices are discarded, and an offset correction is applied to correct for remaining contributions. Jet energy corrections are derived from simulation to bring the measured response of jets to that of particle-level jets on average. In situ measurements of the momentum imbalance in dijet, photon+jets, Z+jets, and multijet events are used to account for any residual differences in jet energy in data and simulation.
The statistical uncertainty is small in comparison to the systematic uncertainties in all bins. The data in the three channels are combined before unfolding in order to model correlations between channels and reduce statistical uncertainties in poorly populated regions of the unfolding matrix. For some observables, both the absolute and normalised differential cross sections are measured at both the particle level in a fiducial phase space and at the parton level in the full phase space. This leads to four measurements for each of these observables. The observables related to the kinematics and multiplicities of jets and leptons are determined at the particle level only.
Systematic uncertainties The systematic uncertainties in the measured differential cross sections are categorised into experimental uncertainties arising from imperfect modelling of the detector response and conditions and theoretical uncertainties arising from the modelling of the signal and background processes. Each systematic uncertainty is determined separately in each bin of the measured differential cross section via a variation of the corresponding aspect of the simulation setup. A regularised unfolding method, described in Section 5, is used to correct for the migration of events between bins due to the finite detector resolutions and to extrapolate the detector-level data to the fiducial and full phases spaces. The variations are applied both at detector level and in the response matrices that define the unfolding.
Experimental sources of uncertainty In order to account for the differences in trigger efficiencies between data and simulation, scale factors, defined as the ratio of the efficiencies measured in data and simulation, are calculated in bins of lepton η and p T and applied to the simulation. The efficiencies of the dilepton triggers in data are measured as the fraction of events passing triggers based on a p miss T requirement that also satisfy the dilepton trigger criteria. As the efficiency of the p miss T requirement is independent from the dilepton trigger efficiencies, the bias introduced by the p miss T requirement is negligible. The efficiencies are close to unity in both data and simulation. An uncertainty arising from the modelling of the trigger efficiencies in simulation is estimated by two variations of the scale factors.
First, the scale factors are varied within their uncertainties coherently for all leptons. Second, to account for potential differential effects not covered by the coherent variations, simulated events are divided into categories according to the η of the leptons, and the scale factors are varied in opposite directions for each category. A final trigger uncertainty is derived by taking the maximal deviation produced by the two variations in each bin. The uncertainties from modelling of the lepton identification and isolation efficiencies are determined using the tag-and-probe method with Z+jets event samples  [61, 67]. The differences between lepton identification and isolation efficiencies in data and simulation in bins of η and p T are generally less than 10% for electrons, while differences for muons are negligible.
6%, corresponding to the uncertainty in the measurement of this cross section presented in Ref. [69]. The uncertainty due to imperfect modelling of the b tagging efficiency is determined by varying the measured scale factor for b tagging efficiencies within its uncertainties. An additional shape uncertainty is determined by dividing the b jet distributions in p T and η at their medians to form two bins in each variable. The b tagging scale factors in the first bin are scaled up according to their uncertainties, while those in the second bin are scaled down and vice versa. The variations are performed separately for the p T and η distributions, and independently for heavy-flavour (b and c) and light-flavour (u, d, s, and gluon) jets. The efficiency of the kinematic reconstruction of the top quarks is found to be consistent between data and simulation within around 0.
Summary Measurements of differential tt cross sections using events containing two oppositely charged leptons produced in pp collisions at a centre-of-mass energy of 13 TeV are presented. The data were recorded with the CMS detector in 2016 and correspond to a integrated luminosity of 35. 9 fb -1. The differential cross sections are presented as functions of numerous observables related to tt production and decay and are based on both particle-level objects in a phase space close to that of the detector acceptance and parton-level top quarks in the full phase space. For each observable, absolute and normalised differential cross sections are presented. Most measured differential cross sections are well modelled by theoretical predictions. However, significant disagreement between the data and Monte Carlo simulation with next-to-leadingorder (NLO) precision in quantum chromodynamics is observed for the transverse momentum of top quarks, leptons, b jets, tt, ¯ , and bb systems, and the invariant mass of the tt, ¯ , and bb systems.
Predictions with beyond-NLO precision are generally in closer agreement with the data although some significant discrepancies remain. The jet multiplicity distribution is not well described by any of the Monte Carlo predictions. The absolute particle-level differential cross section as a function of ∆φ( , ¯ ) is used to constrain the top quark chromomagnetic dipole moment at NLO precision in quantum chromodynamics using an effective field theory framework. The tt and leptonic charge asymmetries are measured using 13 TeV data for the first time and found to be in agreement with standard model predictions.
Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F. R. S. -FNRS and FWO (Belgium) under the "Excellence of Science -EOS" -be. h project n. 30820817; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lend ület ("Momentum") Programme and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the Council of Scientific and Industrial Research, India; the HOMING PLUS programme of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus programme of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa de Excelencia María de Maeztu, and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programmes cofinanced by EU-ESF, and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).
, 2013). The development of satellite technology and the globalization of environmental datasets have enabled the establishment of a global biogeography. A division of the marine ecosphere into biomes (i. e. a large ecosystem primarily controlled by climate) and provinces has been proposed by Alan Longhurst  (Longhurst, 2007). Four primary biomes (Polar, Westerlies, Trades, and Coastal) and 56 secondary provinces have been identified. This partition of the marine ecosphere was mainly based on the characterization of the seasonal cycle of primary production  (Longhurst, 2007). Variables used to establish the partition were chlorophyll-a concentration, mixed layer depth, nutrients, the Brunt-Vaisala frequency, the Rossby radius of internal deformation, photic depth, algal biomass and primary production.
Physical data We used physical data originating from Bio-ORACLE v2. 0 (Marine data layers for ecological modelling)  (Assis, Tyberghein, Bosh, Verbruggen, Serrão et al. , 2017; Tyberghein, Verbruggen, Pauly, Troupin, Mineur et al. , 2012). Bio-ORACLE is a global dataset consisting of 23 geophysical, biotic and climate rasters. This data package for marine species distribution modelling is available for download at http://www. bio-oracle. org. For this purpose, we used both minimum and maximum sea ice concentration (fraction), sea surface temperature (°C), salinity (PSS), bathymetry (m), light at the seabed (E. m -2. yr -  ), Nitrate, phosphate and silicate (mol. m -3 ), Photosynthetically Active Radiation (PAR; E. m -2. day -  ), chlorophyll concentration (mg. m -3 ), primary production (g.
km). We used another dataset to test the influence of the difference in temporal coverage on the ecological partition. These annual SSTs originated from the dataset  ERSST_v3 (1946 ERSST_v3 ( -2015)). The dataset is derived from a reanalysis based on the most recently available International Comprehensive Ocean-Atmosphere Data Set (ICOADS). Improved statistical methods have been applied to produce a stable monthly reconstruction, on a 2° x 2° spatial grid, based on sparse data  (Smith, Reynolds, Peterson & Lawrimore, 2008). Data were interpolated on a global grid of 1° latitude x 1° longitude.
Biological data The Continuous Plankton Recorder (CPR) Survey is a long-term, sub-surface marine plankton monitoring programme consisting of a network of CPR transects towed monthly across the major geographical regions of the North Atlantic. It has been operating in the North Sea since 1931 with some standard routes existing with a virtually unbroken monthly coverage back to 1946  (Batten, Clark, Flinkman, Hays, John et al. , 2003; Reid, Colebrook, Matthews, Aiken, Barnard et al. , 2003). The CPR survey is recognised as the longest sustained and geographically most extensive marine biological survey in the world. The dataset comprises a uniquely large record of marine biodiversity covering ~1000 taxa over multi-decadal periods. The survey determines the abundance and distribution of phytoplankton and zooplankton (including fish larvae) in our oceans and shelf seas.
The threshold used for salinity was the third quartile and the threshold used for current velocity was the 9 th decile based on all marine areas of the world. Pelagic habitats species or taxa were considered for the period 1946-2015 (a total of 254,410 CPR samples), which represented a total of 60,549,580 data points. We partitioned the North Atlantic Ocean and its adjacent seas using two spatial resolutions: (1) a grid of 2° latitude x 2° longitude that enabled a large coverage into the North Atlantic Ocean despite the lower CPR sampling coverage, and (2) a grid of 0. 5° x 0. 5° from 40. 5°N to 65. 5°N and from 80. 5°W to 9. 5°E that enables a finer partition in seas around the British Isles where CPR sampling is the densest. For the two biological partitions, we first estimated the species richness of each taxonomic group on the two spatial grids.
In addition, we calculated an index of cell group heterogeneity H=[hi,j]. For each geographical cell, we calculated the percentage of cells that belonged to different groups of geographical cells, which is the number of different groups v-1 (maximum of nine cells; here also the target cell and all 8 adjacent geographical cells) divided by the number of classified cells w-1 (maximum of nine cells). The index was therefore calculated as follows: hi,j=(100 (v-1))/w-1 (4) For example, for nine possible cells, the index of heterogeneity is 0% when only one group is present and 100% when each cell belonged to a different group. A total number of five cells was needed to have an estimation of the heterogeneity of a cell. The results of this analysis are in Figures  7B  and 8B. All procedures were programmed in Matlab.
Potential influence of the difference in temporal coverage between the habitat and the biological partitions The biological partition was based on CPR data sampled during the period 1946-2015. The habitat partition was based on the BIO-ORACLE v2 dataset, which encompassed the period 2000-2014. The ecological partition is similar to the biological partition but with the polar biome divided into 3 EUs using information from the habitat partition; this subsequent division was made because of the poor CPR sampling occurring in this area (PO, SPO and HSO in Fig. 9 ). We merged the two (biological and habitat) partitions for a restricted region corresponding to the Polar biome between  ~50°N and ~66°N and ~55°W and ~5°E and  thereby the difference of time periods may have only affected the boundary between PO and SPO and SPO and HSO (Fig.
9 ). Using ERSST data, we compared annual SST data based on  1946-2015 and 2000-2014  and no substantial differences in SST (<0. 5°C) were found in the areas where the PO/SPO and SPO/HSO boundaries are located (Fig. 2 , Fig. 9  and Supplementary Fig. 2 ). Because annual SST and sea ice concentration are highly correlated, we conclude that the consideration of the two time periods did not affect substantially our ecological partition. In addition, we assumed that the spatial changes in salinity was also limited between the two time periods but no dataset was available to us for testing.
The CPR survey collects about 5000 samples every year, which is unique in the world at such spatio-temporal scales and levels of taxonomic resolution. However, it remains too limited to give a dynamic picture at the same spatial resolution at a year-to-year scale. Nevertheless, an examination of decadal changes in the ecoregions is achievable in many areas sampled by the CPR survey  (Planque & Fromentin, 1996; Reid et al. , 1998; Richardson & Schoeman, 2004). Biological data are becoming available at a global scale thanks to initiatives such as OBIS. However, even those datasets remain insufficient to provide a dynamic picture of the epipelagic system at a large scale and at a relatively high spatial resolution. Second, some EUs or ecoregions were poorly sampled by the CPR survey (Figure  1 , Tables  2 3 and Supplementary Tables  7 8 ), which may have affected our partition.
Acknowledgments The CPR Survey is an internationally funded charity that operates the CPR programme. The CPR survey operations and routes are funded by a funding consortium from the UK, USA, Canada and Norway. Within the UK, government organisations DEFRA and NERC contribute to core operations. Part of this research was funded by the European research BG-8 programme AtlantOS. surface current (m. s -1 ). N: mean nitrate concentration (mol. m -3 ). P: mean phosphate concentration 1316 (mol. m -3 ). Sil: mean silicate concentration (mol. m -3 ). PAR: mean photosynthetically active radiation 1317 (E. m -2. day -  ). C: mean chlorophyll concentration (mg. m -3 ). PI: mean primary production (g. m -3. day -  ). 1318.
Acknowledgements We thank  Axel Schambach  and  Chistopher Baum  for the gift of the pSERS11 SF GFP pre plasmid,  Stephen Hughes  and  Andrea Ferris  for the gift of RCASBP(A) and RCANBP(A) CMV GFP plasmids, and to  Philippe Ravassard  for sharing the pENTER-TVA vector. We are also indebted to  Karine Bailly  ( Cochin's CYBIO facility ), and  Chantal Bécourt  for their help in flow cytometry,  Romain Fragnoud  ( Texcell, Evry, France ) for assistance in retroviral copy number determination, and  Marc Diedisheim  for help in statistical analyses.
Fundings This work was supported by  ANR-16-CE18-0005-01  (BromoΒ). The RS laboratory is supported by  The Foundation Bettencourt Schueller , belongs to the  Laboratoire d'Excellence consortium Revive  and to the  Departement Hospitalo-Universitaire (DHU) Autoimmune and Hormonal disease. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Nevertheless, variation may reveal regional-specific effects that modulate such species and community responses, thus enhancing our understanding to inform regional forest management. To assess if bird community-forest type relationships are consistent between regions, we compare bird communities in semi-natural oak (Quercus spp) forests and conifer plantations, and use this spatial variation to evaluate differential bird responses to these two types of forest. To assess whether these responses are consistent among regions, we compiled data from three regions (Ireland, France and Portugal) representing distinct Atlantic biogeographic areas of Europe. Specifically, we examined (a) if bird community composition and diversity (taxonomical, functional and phylogenetic) in conifer plantations differed from semi-natural forests in each study region, (b) if these responses were consistent across study regions, and (c) whether consistent trait-environment relationships determined which species responded positively or negatively to plantation forests.
Study regions and data harmonization We recorded forest bird communities from three study regions located in Ireland, France and Portugal, within the Atlantic biogeographical region of Western Europe (see S1 Table for regional environmental characteristics). Data for each study region were originally collected as part of independent studies  [13, 28, 29] , hence, for consistency among datasets we restricted the original datasets to only two forest types, extracting the data from closed-canopy monoculture stands composed of even-aged native and non-native conifer species (hereafter conifer plantations), and from mature native semi-natural deciduous forests dominated by oak species (hereafter semi-natural oak forests). Semi-natural oak forests constitute the climax vegetation of the three study regions and are currently under reduced or no management.
Taxonomic and functional diversity Functional traits for the 59 bird species recorded in the dataset (S2 Table ) were used to calculate functional diversity metrics (Rao's quadratic entropy, which is a modification of Simpson's diversity index to incorporate functional differences between species, and functional dispersion, which represents the functional dissimilarity among species in a community) across the three study regions, using the 'FD' package  [34]. We selected species traits on the basis of their expected role in shaping species responses to different environments  [35]  (i. e. semi-natural forest and conifer plantation). Specifically, we selected response-mediated traits regarding (1) body mass, (2) resource use and acquisition (diet type, main foraging substrate and bill length), (3) habitat specialization (habitat affinity and nesting location), (4) reproductive effort (clutch size), (5) life span, (6) migratory status, and (7) home range-size during the breeding season.