dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, to reduce the potential impact of outliers, we analyzed samples with R : S falling between the 1st and 99th percentiles, which consist of 9589 samples with R : S ranging from 0. 05 to 2. 47 and a mean of 0. 47 and a median of 0. 36. Sample filtering slightly deteriorated model performance and had a minor impact on the final global root biomass prediction (145 from whole samples vs. 142 Pg from filtered data). We chose root biomass as our target variable instead of R : S because big and small trees contribute equally to R : S, while big trees are relatively more important in biomass quantification. In our observation database, we have more samples that are small woody plants (Supplement, Fig. S6 ). We furthermore split the in situ-measured shoot biomass into three groups, namely measurements with shoot biomass smaller than 0.
Uncertainty quantification We estimated the overall uncertainty in the root biomass estimates through quantifying errors caused by predicting root biomass at the 1 km resolution (η pred ) and converting root biomass per tree to root biomass per unit area (η con ). We quantified the prediction uncertainty through an ensemble of predictions. We collected eight additional global predictor datasets (three shoot biomass, two soil and three climate datasets) (Supplement, Table  S2 ) and carried out 8 × 4 (4 folds) sets of additional predictions replacing the predictors by each of these additional data maps. We calculated the standard deviation among 36 predictions for each pixel (Supplement, Fig. S4a ). Converting root biomass from per tree to per area is performed through the tree density  (Crowther et al.
Relative importance of predictor variables The impact of predictors on predicting R : S was estimated through Spearman's rank-order correlation at both the global and the biome scales. We log-transformed the R : S and shoot biomass before standardizing these datasets. Partial dependence plots  (Hastie et al. , 2009)  show the marginal effect that one predictor has on root biomass from a machine learning model and serves as a supplement to the Spearman correlation.
Data availability Raw datasets and global maps generated in this study are deposited at the open-access repository Figshare (https://doi. org/10. 6084/m9. figshare. 12199637. v1; Huang et al. , 2020). The source data and code underlying Figs. 1 2 3 and S2-S17 are also provided at Figshare. Santoro (2018). See the Supplement, Table  S7 , for details. d This value is converted from 94 Pg C to 188 Pg of biomass assuming a 50 % carbon content of tree biomass. e RD a , the relative difference in Tr + Te + Bo between this study ( a ) and previous quantifications. RD a = (previous studythis study) / this study ×100 %. For example, in the column with the heading Jackson, RD a = (200 -139)/139 × 100 % = 44 %. f,h Relative to our machine learning estimate with 0 % tree cover threshold as the forest definition, i.
e. , weighted mean R : S). These weighted mean R : S values, ranging between 0. 19 and 0. 31 across biomes (Supplement, Table  S3 ), are generally smaller than the R : S values reported in previous studies, which were based on average ratios obtained from sparser data (Supplement, Table  S5 ). Note that the arithmetic mean R : S values (without weighting by biomass) from woody plants located in tropical, temperate and boreal zones (Supplement, Table  S4 ) from our database are close to those from Robinson (2007) (Supplement, Table  S5 ). And our predicted patterns of relatively large R : S in regions such as dry forests, savannas and boreal tundra woodlands (Fig. 1c ) are in line with data compiled from  Mokany et al. (2006)  and the 2019 IPCC refinement  (IPCC, 2019). Spawn et al. (2020)  estimated root biomass from shoot biomass and the correlation between root : total biomass and temperature.
2a, c ), but smaller plants tend to have larger R : S (Fig. 2e ; see also  Enquist and Niklas, 2002, and Zens and Webb, 2002). This sampling bias shifts the R : S towards larger values. If we use the biome-level mean R : S from our in situ database (Mean (Obs) in the Supplement, Table  S4 ), multiplying the shoot biomass (Supplement, Table  S3 ) yields a global value of 233 Pg, larger than using the mean R : S across grid cells through RF (155 Pg). Our RF approach uses in situ data for training, but in the upscaling, it accounts for realistic distributions of plant size (Supplement, Fig. S5 ; Supplement, Table  S4 ). We further verified that our upscaled R : S ratios are robust to sub-sampling the training data in observed distributions so that the bias of training data towards small plants does not translate into a bias of upscaled results (see Method and the Supplement, Fig.
S8 ). The upscaling approach using allometric equations should also tend to overestimate (see the Supplement, "Allometric upscaling" section) the global root biomass due to the curvature of these allometric functions  (Enquist and Niklas, 2002; Zens and Webb, 2002). The global forest root biomass ranged between 154-210 Pg when root biomass was upscaled through different allometric equations collected from the literature and fitted to our database (Supplement, Table  S7 ), which is generally larger than from the RF mapping. The global root biomass is likely to be smaller than when applying the allometric equation to the spatial average of shoot biomass (Supplement, Figs. S14-S17 ). Thus, future in situ characterization of the distribution of tree sizes across the world's forests (see the Supplement, "Allometric upscaling" section) would greatly improve root biomass quantification.
Note that how well our global estimate reflects the real root biomass is conditioned upon the accuracy of the in situ root measurement database used to train our RF model. Undersampling is a common issue in many root studies due to the fractal distribution of root systems in soils and the difficulty of implementing an efficient sampling strategy  (Taylor et al. , 2013) , especially for large trees. We did not quantify the uncertainty in our estimates associated with in situ root measurements due to lack of reliable information. An accurate spatially explicit global map of root biomass helps to improve our understanding of Earth system dynamics by facilitating fundamental studies on resource alloca-tion, carbon storage, plant water uptake, nutrient acquisition and other aspects of biogeochemical cycles.
c Estimation based on allometric equations and the global aboveground biomass dataset from Santoro et al. (2021) and This This Jackson Saugier Robinson Spawn This study a study b (Jackson et al. , 1997) (Saugier et al. , 2001) (Robinson, 2007) (Spawn et al. , 2020) study c Method Machine Machine Biome average root Biome average R : S, Biome average R : S, Relationships between Allometric learning learning biomass density shoot biomass density shoot biomass density root : total biomass and temperature equations Tropical (Tr; Pg) 92 76 114 147 246 Temperate (Te; Pg) 26 25 51 59 98 Boreal (Bo; Pg) 21 20 35 30 50 Tr + Te + Bo (Pg) 139 121 200 236 394 Globe (Pg) 142 142 188 d 155-210 RD a,e 0 % 44 % 70 % 183 % 24 % f RD b,g 0 % 65 % 95 % 226 % 24 % h a.
VANDELS The majority of spectra used in this study are from VANDELSa deep VIMOS survey of the CANDELS UDS and CDFS fields -which is a recently completed ESO public spectroscopic survey carried out using the VLT. The VANDELS survey covers two wellstudied extragalactic fields, the UKIDSS Ultra Deep Survey (UDS) centred around RA = 02:17:38, Dec = -05:11:55, and the Chandra Deep Field South (CDFS/GOODS-S) centred around RA = 03:32:30, Dec = -27:48:28. As mentioned earlier, we use VANDELS spectra in the CDFS/GOODS-S field in this study. The survey description and target selection can be found in  McLure et al. (2018) , and detailed data reduction and redshift determination steps can be found in  Pentericci et al. (2018). VANDELS spectroscopic targets were selected based on photometric redshifts (or the presence of Lyman breaks in their broadband photometry) derived from deep ground and space-based multi-band photometry.
The parent spectroscopic sample included bright starforming galaxies ( ≤ 25. 0 AB) in the range 2. 4 ≤  ≤ 5. 5 with 99% of this subsample having specific star-formation rates (sSFR) > 0. 6 Gyr -1 , fainter Lyman-break galaxies (LBGs;  ≤ 27. 5 AB) in the redshift range 3. 0 ≤  ≤ 7. 0 with sSFR > 0. 3 Gyr -1 and passive galaxies with sSFR < 0. 1 Gyr -1  (McLure et al. 2018). Over the redshift range of interest for this study, the VANDELS galaxies are largely sampled from the star-forming main sequence. The latest VANDELS data release, DR4  (Garilli et al. 2021 ) 2 contains spectra of ∼ 2100 galaxies in the redshift range 1. 0 <  < 7. 0, with on-source integration times ranging from 20 to 80 hours, where > 70% of the targets have at least 40 hours of integration time. The survey depths have been chosen so that the final spectra have high signal-to-noise ratios (SNRs), enabling detailed absorption and emission line studies, the study of ionising sources using emission line ratios, the derivation of accurate metallicities and better constraints on physical parameters such as stellar masses and star-formation rates.
The spectral resolution of the final spectra is  ∼ 600. The spectra used in this study were reduced using the E data reduction pipeline  (Garilli et al. 2012)  and the final data products delivered by VANDELS include the extracted 1D spectra, the 2D re-sampled and sky-subtracted spectra and catalogues with physical parameters, including spectroscopic redshifts. The reliability of redshifts in the VANDELS database is recorded using the following flags: 0 -no redshift could be assigned, 1 -50% probability to be correct, 2 -70-80% probability to be correct, 3 -95-100% probability to be correct, 4 -100% probability to be correct and 9 -spectrum shows a single emission line. The typical accuracy of spectroscopic redshift measurements is ∼ 150 km s -1. Details about the final VAN-DELS data products, redshift determination processes and redshift reliability flags assignment can be found in  Pentericci et al.
VUDS The VIMOS Ultra-Deep Survey (VUDS; Le  Fèvre et al. 2015)   3  is a survey of ∼ 10000 faint galaxies in the redshift range 2 <  ≤ 6 with -band magnitudes down to  ≈ 27. 0 AB. The spectroscopic targets were also selected based on either reliable photometric redshifts or the presence of a Lyman break (LBGs) at higher redshifts, similar to VANDELS. From the VUDS survey, we select sources in the GOODS-S/CDFS field with redshift reliability greater than 95%, where the redshift reliability flags are determined in a similar manner to VANDELS. We refer the reader to Le  Fèvre et al. (2015)  and  Tasca et al. (2017)  for more information about source selection, data reduction and redshift measurements. In total we select 20 sources with reliable spectroscopic redshifts from VUDS.
0 AB in HST/F775W. This blind galaxy sample is therefore highly complementary to the photo- or LBG selection implemented by other data sets used in this study. From this survey, we select sources with redshifts 3. 11 <  < 3. 53 and reliable spectroscopic redshifts, i. e. a 'confidence' value of 2 or 3, corresponding to an expected error rate of 10 % in the correct identification of emission and absorption features  (Urrutia et al. 2019). We find that 44 galaxies in the MUSE-WIDE data release satisfy our selection criteria.
SED derived physical properties To determine both the properties of stellar populations as well as the [O ] + H line strengths using  band photometry, we simultaneously fit the stellar continuum and nebular line emission to the observed broadband photometry of galaxies in our sample. We note that since both stellar continuum and nebular emission are being simultaneously fitted, we include the line-contaminated  band photometry in the fitting. The SED fitting is performed using the Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation (B ) code  (Carnall et al. 2018 ). B is a based tool to generate complex galaxy model spectra, allowing flexible fitting to photometric data to derive key galaxy physical properties. As mentioned earlier, we use a combination of stellar continuum and nebular emission when fitting the SEDs.
SUMMARY AND CONCLUSIONS We have measured the Lyman continuum (LyC) escape fraction (  esc ) using ground based LyC images available for 183 spectroscopically confirmed galaxies in the CDFS/GOODS-S field in the redshift range 3. 11 <  < 3. 53. The spectroscopic redshifts are taken from publicly available databases, that primarily targeted the higher mass Lyman break (LBG) selected galaxies or blindly discovered lower mass galaxies thanks to emission line surveys. We use available deep HST photometry and colours at non-ionising UV wavelengths to remove possible foreground contaminants that may strongly bias the inferred LyC flux and  esc as well as accurately constrain the spectral energy distributions (SEDs) of our galaxies giving robust measurements of key galaxy properties. Using a combination of stellar and nebular emission, we also derive rest-frame optical emission line strengths, primarily the [O ] and H line fluxes (that fall in the observed  band) for our galaxies.
ACKNOWLEDGEMENTS We thank the referee for constructive feedback that improved the quality of this work. AS thanks  Ross McLure  and  Mario Nonino  for useful input and discussions,  Fergus Cullen  for sharing NIRVAN-DELS measurements and  Mengtao Tang  for sharing the best-fitting relation for emission line strengths from their analysis. AS and LP thank  Alice Shapley  for useful comments and discussions that improved the analysis presented. AS and RSE acknowledge financial support from  European Research Council Advanced  Grant  FP7/669253. DS and ASL acknowledge support from  Swiss National Science Foundation. RA acknowledges support from  ANID FONDECYT Regular 1202007. MLl acknowledges support from the  National Agency for Research and Development (ANID) / Scholarship Program /Doctorado Nacional/2019-21191036.
DATA AVAILABILITY The VANDELS data can be accessed using the VANDELS database at http://vandels. inaf. it/dr4. html, or through the ESO archives. The VUDS data can be accessed from the VUDS webpage: http://cesam. lam. fr/vuds/ or ESO archives. MUSE data can be accessed from https://musewide. aip. de/project/ and http://muse-vlt. eu/science/udf/ and the 3D-HST data can be accessed from https://archive. stsci. edu/prepds/ 3d-hst/. The VIMOS10 data are available from Balestra et al. (2010). All the code used to perform the analysis in this paper was written in and will be shared upon reasonable request to the corresponding author.
| Study sites The study was conducted at six sites along a climatic gradient in the French Alps (Figure  1 ;  Jourdan, Piedallu, et al. , 2020). All sites are characterized by limestone bedrock, with a North to West orientation (Table  S1 ). Minimum and maximum temperature and precipitation were recorded by meteorological stations at each site, and the gaps were filled by E-OBS gridded dataset (2005-2019) (https:// www. ecad. eu/downl oad/ensem bles/downl oad. php). In the three Northern sites (BG, VC and VT), the forest is dominated by beech (Fagus sylvatica L. ) and silver fir (Abies alba Mill. ) trees, and in the three Southern sites (LA, LU and SB), the dominant trees species are beech and downy oak (Quercus pubescens Willd. ; Table  1 ). Other tree species growing at the sites are Acer pseudoplatanus L.
| Wood anatomical traits We cut a 20-cm-long twig sample (70 cm from the apex, diameter between 2 and 3 cm) from the top of the canopy of each tree to determine wood properties. The samples were stored in plastic vials with a 50% ethanol solution. Transversal sections of each sample (12 µm thick) were made with a Leica rotation microtome and stained with Safranin-O and astra blue (1% and 0. 5% in distilled water respectively). The sections were mounted with Canada balsam on glass slides with a cover slip  (von Arx et al. , 2016). Microscopic digital images were captured at 40× magnification with a compound microscope (BX51, Olympus, Germany) interfaced with a Canon camera (Canon EOS 1200D, Switzerland). Digital images were taken on a radial path (Figure  S1a ). Image analyses were performed with the ImageJ software.
| Leaf morphological traits Leaf morphological traits were assessed by sampling 10 leaves from long terminal shoots on one branch from the top canopy  (Cornelissen et al. , 2003). The mean leaf surface (LS, cm 2 ) was determined using scanned fresh leaves (EPSON Perfection V800 Photo, EPSON, Amsterdam) and the Silver Fast 8 software (Laser soft imagine AG, Germany). After drying for 48 hr at 65°C, the leaves were weighed, and the specific leaf area calculated (SLA, m²/kg).
| Data analysis A climatic index considering the long-term variation in mean annual temperature and precipitation along the gradient was calculated by dividing the precipitation by the potential evapotranspiration (P/ PET). PET at each site was calculated using the Thornthwaite equation  (Kolka, 1998) , which takes into account the average daily temperature, the number of days per year (i. e. 365), the average day length and a heat index estimated using monthly mean temperature. P/PET ranged between 3. 31 and 1. 39 over the last 20 years (from 2005 to 2019) and between 0. 61 and 0. 08 over the measurement period (from July to August 2019) for the wettest and the driest site respectively (Table  1 ). We determined the effect of P/PET (average from 2005 to 2019), species mixture (pure vs. mixed stands) and the interactive effect of P/PET and species mixture all along the gradient on each functional trait using linear mixed models (lme4 and lmerTest function).
Which underlying processes are driving these differences remains an open question for future work. Disentangling the impacts of atmospheric and soil drivers on trait variation would deserve further studies to fully decipher the significance of species interactions under a changing climate. ACK N OWLED G EM ENTS M. D. -G. and C. G. were supported by the Swiss National Science Foundation SNF (PZ00P3_174068). C. G. is supported by the Sandoz Family Foundation. We are grateful to the two tree climbers J. Baudry and E. Norois, for their help during the fieldwork. We thank C. Donzé for her contribution to assessments and data acquisition in the field and the laboratory. The plot design used in this study is part of the GMAP plot network (https://oreme. org/ obser vatio n/foret/ gmap/), partly funded by the OSU OREME in Montpellier, France.
Funding The research leading to the results presented in this publication was partly carried out with infrastructure funded by the Direction G en erale de la Recherche Scientifique et du D eveloppement Technologique(DGRSDT) and by the Laboratoire de Biodiversit e et Environnement: Interactions -G enomes (LBEIG), Universit e des Sciences et de la Technologie Houari Boumediene (USTHB), Algiers, Algeria. This study was also supported by the Institut de Syst ematique, Evolution, Biodiversit e (ISYEB), Mus eum national dʼHistoire naturelle (MNHN) Paris, France), and a framework agreement project of the DeepBlue Project: Distance Crossborder Traineeship Programme co-financed by the European Maritime and Fisheries Fund (EMFF) for the analysis, interpretation of data and the writing of the manuscript.
Commercial software Comsol Multiphysics Ò (COMSOL Inc. ) was employed to perform the hydrothermal numerical analyses. Both soil and concrete were considered as porous media composed of a solid matrix and a liquid phase  (Bourne-Webb & da Costa Goncalves, 2016; Dean, Montes, Valavala, & Haselbach, 2005; Diamond, 1999; Dupray, Laloui, & Kazangba, 2014; Dupray, Li, & Laloui, 2014; Kearsley & Wainwright, 2001 , 2002; Kumar & Bhattacharjee, 2003). The hydrothermal coupling in the porous media was considered through the local thermal equilibrium hypothesis, which assumes the pointwise equality of temperature in solid and liquid phases  (Nield & Bejan, 2006; Nield, 1998). The pipes were composed of tubes filled with a fluid in motion. The governing equations are the following. The mass conservation equation is applied to the fluid phase of the porous materials: @ @t nq w ð Þþdivðq w v rw Þ ¼ 0; ð1Þ where t is the time, q w is the fluid density, n is the porosity, and v rw is the fluid velocity defined by Darcy's law, which can be written by considering the hypothesis of homogeneous and isotropic porous media as follows: v rw ¼ Àkgrad z þ p w c w ; ð2Þ where k is the hydraulic conductivity, p w is the fluid pressure, and c w ¼ q w g, where g represents the acceleration due to gravity.
and offshore wind farms  (Lascelles et al. 2012 , Harrison et al. 2018 , Hays et al. 2019). Traditionally, spatial data are collected on surveys where organisms are counted and mapped in a given area. For migratory animals, the provenance, or origin, of the observed individuals is often unknown  (Perrow et al. 2015 , Sansom et al. 2018) , and for survey data, it might be difficult to link a spatial environmental risk or management measure to the potential impacts on population dynamics. For example, without knowledge of provenance, it can be challenging to identify the breeding populations affected by an oil spill that kills seabirds at sea during the nonbreeding period (e. g. Cadiou et al. 2004 ). Similarly, it can be difficult to assess the population consequences of bycatch in offshore fisheries of e.
2021 , this Theme Section) (Fig. 1 ). In combination with data on seabird colony sizes across the study area, these tracking data provide a unique opportunity to model not only habitat 256 Fig. 1. Seabird colonies with seabird position data sets (GLS data) used to model the distribution of Northeast Atlantic seabirds utilization but also ex tend the modelling approach to model seabird abundances for populations across the Northeast Atlantic for multiple seabird species. In this study, we combined position data from the SEATRACK database with data on the marine environment and seabird population data to model the year-round spatial distribution of Northeast Atlantic populations of 6 pelagic seabird species: northern fulmar Fulmarus glacialis, black-legged kittiwake Rissa tridactyla, common murre Uria aalge, thickbilled murre Uria lomvia, little auk Alle alle and Atlantic puffin Fratercula arctica.
These species represent the most numerous seabird species breeding in the Northeast Atlantic  (Barrett et al. 2006 , Frederiksen 2010). However, due to recent population declines, several of the species are listed in the European Red List of Birds  (BirdLife International 2015). These are the northern fulmar (endangered), blacklegged kittiwake (vulnerable) common murre (near threatened) and Atlantic puffin (endangered). While all 6 species are long-lived pelagic seabirds, they dif-fer in trophic level and foraging mode  (Del Hoyo et al. 1992) , and it is therefore expected that they will utilize different habitats and show different responses to the marine environment and potential stressors. The workflow of our analyses is shown in Fig. 2. Position data from the SEATRACK database were combined with environmental data to model the monthly habitat use of the species from each SEA -TRACK colony.
Environmental variables were derived from data on bathymetry and remote sensing data of sea surface temperature (SST), sea surface height and primary production. We used crossvalidation to assess how the models performed when predicting the spatial distribution of birds in other years and birds from other colonies, and we used cluster bootstrap sampling to estimate the CIs of the predictions. To link the habitat models to seabird populations, we compiled a colony database for the Northeast Atlantic and conducted spatial analyses to justify distance rules for assigning the populations in the colony database to the nearest modelled seabird.