dataesr/scientific-paragraphs-categorization huggingface dataset:
We retained input variables presenting enough occurrences to justify their presence in the dataset. For example, we chose the biochemical composition determined by proximate analysis according to the Van Soest method  [1 , 2]  and did not keep the data on lignin obtained by the Klason method  [6]  because they were very scarce. We also did not retain the data on sugar concentrations (glucose, sucrose, fructose, etc. ), which were rarely available in the literature, and we did not retain data on lipids, resins and starch contents. We have encountered several situations where the common names differed for the same species, while the Latin name assigned by the authors was identical. In these cases, we have chosen to group them under the same Latin name item (e. g. , Raphanus sativus L. correspond to fodder radish, oil radish and daikon radish; Vicia faba L.
The harvest height was rarely specified. When it was, we noted it (e. g. , "aerial DM" versus "aerial DM ( > 5 cm)"). We carried out the same process for the root measurement depth (e. g. , "roots" versus "roots (0-20 cm)"), which was rarely indicated. We performed the mean and standard error calculations whenever possible, specifying the number of observations. When the amount of data collected was too low or the range of the values was too large, we did not calculate means, leaving this action to the users. This dataset is intended to be updated regularly based on new data published in the literature.
Acknowledgements The present study was supported by the  FACCE ERA-GAS Cofund (European Research Areafor mitigation of Greenhouse gases from Agri-and Silvi-culture ; id number:  696356  ), project  ResidueGas  coordinated by  Professor Jørgen Eivind Olesen  ( https://projects. au. dk/residuegas/ ). The French contribution to ResidueGas was funded by the  Agence Nationale de la Recherche  (  ANR-17-EGAS-0 0 03  ) and the  National Institute for Food, Agriculture and the Environment (IN-RAE).
Description of data collection Secondary data : We collected all the elementary data (e. g. , biomass, % N, % C, and lignin content) that were found in the literature. Raw data : The elementary data were aggregated into five agricultural production types: main crops, pasture and forage crops, green manure and cover crops, vegetable crops and energy crops. Means and standard errors of the data were calculated. Data source location Raw data of figures are available as supplementary materials of Data in Brief site. Secondary data: a complete list of the primary data sources used, with their access link, is produced in the "List_of_literature_references_v1. tab" file in repository Data INRAE (see Data accessibility part). For any other file, the complete [dataset] [3] is securely stored in Data INRAE Center located in Jouy-en-Josas (FR) with a replication in the Data Center of Toulouse (FR).
Each "object" (protein, transporter, complex) is clickable, allowing the user to access the data available online but scattered throughout the literature. Currently this reconstructed network is a tool for exploring metabolism which very quickly provides access to integrated knowledge of a particular metabolism (e. g. , see Ascorbate glutathion pathway) (Figure  2 ). ChloroKB differs from other metabolic databases (BioCYC, Uniprot, KEGG) by its high level of data integration, the consideration of subcellular compartmentalisation and thus metabolite transport, the representation of "fine" controls such as complex formation, activity modification by allosteric control or phosphorylation (e. g. see PEP carboxylase regulation). Sub-cellular localisation has been improved compared to the version initially published with a coding which now considers the different cellular membranes and inter-membrane spaces (plasma membrane, endoplasmic reticulum, inner and outer membranes of mitochondria and chloroplast).
Il se présente sous la forme de cartes métaboliques interconnectées (actuellement au nombre de 189) sur lesquels figurent les compartiments subcellulaires, les métabolites, les protéines, les complexes, les réactions et les transports. Chaque « objet » (protéine, transporteur, complexe) est cliquable, permettant à l'utilisateur d'accéder aux données certes disponibles en ligne mais dispersées dans la littérature. Actuellement ce réseau reconstruit est un outil d'exploration du métabolisme qui permet très rapidement d'avoir accès à une connaissance intégré d'un métabolisme particulier (e. g. voir Ascorbate glutathion pathway) (Figure  2 ). ChloroKB se distingue des autres bases de données métaboliques (BioCYC, Uniprot, KEGG) par son niveau élevé d'intégration des données, la prise en compte de la compartimentation subcellulaire et donc du transport de métabolites, la représentation des contrôles « fins » comme la formation de complexes, de modification d'activité par contrôle allostérique ou par phosphorylation (e.
The ARPES spectra were obtained in UHV (base pressure ∼ 5 × 10 -11 mbar), at 80 K. The presented spectra correspond to a summation of data that were separately acquired using linear p-and spolarized light. The energy and angular resolution were set to 10 meV and 0. 1 • , respectively. Low-energy electron diffraction. As-grown graphene samples were characterized using low-energy electron diffraction (LEED) measured in an UHV chamber with background pressure better than 4 × 10 -9 mbar using a rear-view AEP 8011, VG Scientific instrument. LEED images were recorded using the screen voltage and an electron beam (spot size ca. 1mm) energy of 1 kV and 52 eV, respectively. Density functional theory calculations. We performed Density Functional Theory calculations with generalized gradient approximation (GGA) for the exchange-correlation potential, using a localized orbital basis set and a pseudopotential description of the core elec-trons, as implemented in the SIESTA code.
Dimensionality Reduction Dimensionality reduction was applied to compare a full 25-feature data set to 7-feature and 1-feature reduced variations. Feature selection was performed, first through mutual information and second through chi-square tests, to compare the selection impact of different approaches. Both frameworks were executed in Python using the mutual_info_classif and chi2 methods in scikit-learn's feature_selection module  [19]. The only continuous variable in the data set, age, was discretized through bins in the chi-square method and through a k-nearest neighbors approach in the mutual information method; a more comprehensive definition of the latter can be found in the relevant cited work  [20]. Features were extracted using the extent of P value significance in the chi-square framework and mutual information in the alternative method, where the latter can be broadly defined as  [21] : where X is a predictor feature in the data and Y is an outcome of death or recovery.
Mutual Information Feature Reduction This study compares the impact of two dimensionality reduction methods-mutual information and chi-square tests-on model performance. Table  2  outlines the out-of-sample performance of models trained on the full 25-feature data set compared to 7-feature and 1-feature variations extracted through mutual information. All metrics were calculated over multiple repetitions of 3-fold cross validation. Due to differences in class balance across folds and simulated repetitions, accuracy metrics are not necessarily a weighted average of their sensitivity and specificity. We note that performance across all models and datasets is sound, with no accuracy below 79. 2%. The best random forest classifier performed substantially above the best logistic classifier, with respective accuracies of 90.
8% and 83. 5%. In assessing the impact of dimensionality reduction, we note that transitioning from 25 features to 7 improved the performance of the random forest classifier (89. 2% to 90. 8% accuracy), while a minor (considering the extent of feature shrinkage) reduction in performance was observed in the logistic classifier (83. 5% to 79. 2%). The latter may be overstated by the model's decision threshold, as the AUC decrease was minor (88. 6% to 88. 4%). Finally, extreme reduction to a single most important attribute-in most folds, age-resulted in substantially reduced performance in the random forest classifier (89. 2% to 80. 1% compared to the full data set baseline) but more muted loss in the logistic classifier (83. 5% to 81. 5%). All models were drawn from Python's scikit-learn libraries.
(f) The annual regime for 11 large basins of the Amazon, based on HOP data sets  (bars) and the CHIRP based  in magenta lines. (g) Annual average negative (red scale) and positive (blue scale) bias of six precipitation RS-based and non-gauged-corrected products in the Amazon basin for the period 2000-2016, adapted from  (Beck, Vergopolan, et al. , 2017). 3e  show the cumulative rainfall for the annual, wet (DJF) and dry (JJA) period, respectively, for the Amazon basin. In these figures, the Hydro-geodynamics of the Amazon basin Observatory (HY-BAM) observed precipitation data set (HOP), comprised of 752 daily rain gauge stations throughout the Amazon at 1° spatial resolution  (Espinoza et al. , 2016; Guimberteau et al. , 2012) , and the 5 km resolution CHIRP data set, a non-gauged-corrected product, have been used.
g. , Intertropical Convergence Zone  [ITCZ] , South American Monsoon System [SAMS], South Atlantic convergence zone  [SACZ] ;  Espinoza Villar, Ronchail, et al. , 2009). The bias performance of the data sets is shown in Figure  3g , which considers six non-gauged-corrected data sets (PERSIANN-CCS, MSWEP-ng v2, CHIRP v2. 0, CMORPH v1. 0, SM2RAIN-ASCAT, and TMPA 3B42RT v7, adapted from  Beck, Vergopolan, et al. , 2017). The bias of total annual rainfall for the period 2000-2016 is plotted for negative and positive values, where at least one of these databases has detected an equal or greater value of bias. These satellite data sets were validated for the Amazon basin against global and local in situ stations (e. g. , GHCN, the Global Summary of the Day [GSOD] database, the Latin American Climate Assessment & Dataset).
For instance, spatial synoptic changes in rainfall patterns were evaluated using RS information due to the heterogeneous spatial distribution of weather stations and inconsistent temporal measurements of gauge data  (Arvor et al. , 2017; Silva Junior et al. , 2018). Other studies on a daily scale focused on evaluating the performance of the TMPA V7, TMPA RT, CMORPH, and PERSIANN data sets to represent the precipitation concentration index during the period 2001-2009  (Zubieta et al. , 2019). This index is an indicator for temporal precipitation distribution. The authors concluded that the best products (CMORPH and TMPA V7) can be an alternative source of data to detect changes in daily precipitation concentration during dry or wet seasons in regions of the basin that experience extreme events.
, 1999). At seasonal to intraseasonal scales, OLR data from NOAA polar-orbiting satellites was used to identify the intensity and spatial features of the SACZ in the Brazilian Amazon region (L. M. V. Carvalho et al. , 2004). The SACZ is a northwest-southwest convection band that extends from the Amazon basin to the southeastern Atlantic Ocean, and its intensity and geographical distribution are associated with extreme rainfall events in the southern Amazon. At the intraseasonal scale, the large-scale Madden-Julian oscillation (MJO; FASSONI-ANDRADE ET AL. 10. 1029/2020RG000728 13 of 97  Madden & Julian, 1994)  has been established as the dominant mode of variability across the tropics, modulating the SACZ and other climatological features over the basin. Mayta et al. (2019)  and  Vera et al.
, 2009). Some national initiatives, as the Brazilian National Water Resource Information System (SINGREH) and the Meteorological Database for Research from the Brazilian National Water and Sanitation Agency (ANA) and the National Institute of Meteorology (INMET), respectively, and international research projects, as the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA;  Davidson & Artaxo, 2004) , provided standardized hydrometeorological and surface flux measurements to understand energy, water, and carbon exchanges across different tropical ecosystems  (Gonçalves et al. , 2013; Saleska et al. , 2013). However, due to the high cost of eddy covariance measurements and maintenance difficulties, there are only a few towers located across the basin, and these do not cover the whole Amazon climate-vegetation complexity.
, 2005)  -a re-tracker based on fitting the leading edge and the trailing edge slope of radar waveforms to a Brown function-for ERS-1, ERS-2 and ENVISAT, and promoted its delivery in the Geophysical Data Records (data files containing along-track altimeter measurements and the corrections that are needed to be applied to the range in order to retrieve WSE). The retracking of radar echoes was analyzed by  Frappart et al. (2006 Frappart et al. ( , 2016) )  and Da  Silva et al. (2010)  over 70 ERS-2 and ENVISAT VSs and a large range of river widths (from tens of meters to kilometers). They reported that the proper selection of the data considered as representative of the water body is as important as the choice of the retracking algorithm. The data from the 10-day repeat orbit of Topex/Poseidon (T/P) and Jason-2/3 have also been assessed in the Amazon basin.
Such missions, instead of providing a SWE observation on a 10-day or almost monthly basis with a large intertrack distance at the equator (between 60 and 100 km), provide a much denser spatial span but with observations separated from another in time. The use of ICESat (Ice, Cloud, and land Elevation Satellite) laser altimetry data was investigated by  Hall et al. (2012). They concluded that this mission can be a valuable source of data for monitoring rivers from the Amazon, with accuracies of some tens of centimeters when compared to gauges. The ICESat mission was continued by ICESat-2, launched in 2018. Studies by  Bercher et al. (2013)  and  Jiang et al. (2017)  concluded that the SAR mission CryoSat-2 offers new opportunities to monitor narrow rivers in the Amazon basin, and should help linking the present and future altimetry missions.
(2002)  using VSs from the T/P mission. They revealed changes in the sign of the rate of slope variation that were explained by the river not reaching equilibrium. Although the slopes from Birkett et al. (  2002   mant et al. , 2013) , these breaks in slope variation rate were not found in profiles extracted from more recent and complet. altimetric databases  (Calmant et al. , 2016). Paris et al. (2016)  estimated two different time series of slopes from satellite altimetry in the lower Negro River: the first was calculated using a daily interpolation of upstream and downstream SWE time series, providing a daily slope time series, and the second was calculated using the mean climatology of upstream and downstream VSs. Although the stage to discharge relationship was improved when considering the variation of slope with time estimated through both methods, it is the monthly means that provided the best improvement.
The original SWE time series or their conversion into discharge offer an independent tool to validate hydrological models  (Paris et al. , 2016)  and their rainfall inputs, and in situ data  (Da Silva et al. , 2014). With its disruptive technology based on swath altimetry, almost-global coverage and joint observation of SWE, river width and slope, the SWOT mission, to be launched in 2022, will permit an unprecedented observation of SWE all along the river network and on major lakes and floodplains. As highlighted by  Biancamaria et al. (2016) , SWOT observation of SWE will permit a better monitoring of transboundary waters and wetlands in the Amazon. Dedicated to sample all rivers wider than 100 m and lakes larger than 250 × 250 m, the mission will permit a consequent reduction of global and regional models, noteworthy through data assimilation  (Emery et al.
However, to operationally monitor non-open waters such as permanently or seasonally flooded vegetated floodplains remains challenging. In fact, few lakes and reservoirs are monitored by altimetry routinely in the basin though more could be  (Crétaux et al. , 2011; Crétaux & Birkett, 2006). The forthcoming missions will benefit from past research to improve the accuracy of SWE time series and promote its use for monitoring more local phenomena, such as floodplain-channel exchanges. Although limited due to availability of appropriate data, InSAR data sets help characterize floodplains/rivers connectivity and dynamics. The global coverage of the forthcoming SWOT mission will increase greatly our understanding on the global water cycle and should allow a better quantification of past and current inter-mission biases, helping turn satellite altimetry archives into a unique climatic data set and understanding the impacts of climate change and human activities on the basin.
2). Characterizing the distribution and quantifying seasonal and interannual variations in the extent of surface waters at the scale of the Amazon basin is a challenge given their large variety and variability, and the presence of cloud cover and forest vegetation. Early estimates of the distribution of surface water for large areas were based on static databases from aeronautical charts and aerial photographs, which often reflected the maximum open water extent  (Cogley, 2013; Matthews & Fung, 1987)  and did not provide information on their temporal and spatial variations. The Global Lakes and Wetlands Database  (Lehner & Döll, 2004)  estimates the extent of floodplains and wetlands in the Amazon of ∼300-350 × 10 3 km 2 , but with large uncertainties  (Davidson et al. , 2018). The advent of satellite observations now allow monitoring the large-scale dynamic of surface waters, including those in the Amazon basin  (Alsdorf et al.
Further studies and new observations are required to fully characterize Amazon surface water extent and the processes that drive the patterns and dynamics. In particular, polarimetric and interferometric L-band SAR data from the forthcoming NASA/ISRO SAR mission and the Ka-band Radar Interferometer (KaRIn) swath observations from the forthcoming SWOT mission will be capable of enhanced monitoring and comprehensive survey of large-scale surface water extent and dynamics of the Amazon.
, 2006)  generated from C-band interferometry  (Farr et al. , 2007)  and has been widely used in hydraulic simulations and geomorphic characterization of the Amazon floodplains (Figure  7a ). However, the data are affected by vegetation cover and have errors such as absolute bias, speckle noise (granular aspect in the image due to the random presence of pixels with extreme values), and stripe noise  (Rodríguez et al. , 2006). It is also not capable of describing bathymetry of inland water bodies as it observed surface water elevation only once. The application of topographic data, such as SRTM DEM, together with radar (e. g. , RADAM, JERS-1) and optical (e. g. , Landsat) images allowed the geomorphological characterization of floodplains and river channels of the Amazon basin. Sippel et al.
, 2013; O'Loughlin et al. , 2016; Paiva, Buarque, et al. , 2013; Paiva, Collischonn, & Tucci, 2011; Pinel et al. , 2015; Rudorff et al. , 2014a; Yamazaki et al. , 2017) , the interferometric bias  (Pinel et al. , 2015; Rudorff et al. , 2014a) , as well as smoothing and pit removal  (Yamazaki, Baugh, et al. , 2012). Despite the better topographic representation achieved by these methods, topographic information below the water surface cannot be recovered from SRTM. Also, the SRTM data set relies on one only overpass in February 2000. Therefore, some processes, such as infilling and drainage of the floodplain, may not be well represented in the numerical models. River bathymetry is also key information that is not systematically resolved. Recently  Brêda et al. (2019)   The bathymetric information in permanently flooded areas relies on in situ field surveys.
Once the IOPs are known, they are used to retrieve the OAC concentrations. Therefore, SAA algorithms better identify each constituent contribution, providing more comprehensive temporal and spatial coverage  (Dekker, 1993; Novoa et al. , 2017). The flourishing of satellite RS in the second decade of the 21st century is due to two crucial technological advances. First, a new generation of sensors was better designed to study complex aquatic environments, with improved spectral and radiometric resolution (Landsat-8, Sentinel-2, and CBERS-04A). Second, the unprecedented increase in computing performance and data storage has improved image processing capability. However, the low radiometric resolution provided by sensors onboard earlier Landsat (Landsat-5 and Landsat-7) satellites has not prevented the development of studies taking advantage of the substantial temporal database available (1972 to now) as reported in  Lobo et al.
, 2017). However, recent studies have suggested that this process has a relatively low impact on the total ET estimates because of the general energy-limited (and not water-limited) ET in the Amazon  (Fleischmann et al. , 2020; Paiva, Buarque, et al. , 2013). A different conclusion is expected for semi-arid wetlands  (Fleischmann et al. , 2018). Regional scale validation of inundation models has been done with surface water extent  (Getirana et al. , 2012; Luo et al. , 2017; Paiva, Collischonn, et al. , 2013; Wilson et al. , 2007; Yamazaki et al. , 2011)  based on the products by  Hess et al. (2003) , GIEMS from  Prigent et al. (2007) , and more recently with the SWAF database  (Parrens et al. , 2017)  (see Section 4. 2 for a description of these products). Although the flooding seasonal cycle is usually well captured by most models, estimates usually diverge in terms of magnitude  (Fleischmann et al.
, 2021; Maeda et al. , 2017). Various RS-based approaches result in significant divergences in estimating evapotranspiration over the basin  (Figures 4 and 10). For instance, RS-based ET annual rates at the basin scale were 15%-37% higher than those obtained from water balances  (Baker et al. , 2021). The characterization of continental water surfaces, including their elevation and extent, was possible thanks to adaptations of satellite techniques not primarily designed for hydrology or inland water monitoring applications. A striking example is that of altimetry satellite missions, initially designed to observe the ocean, but with promising applications to the large rivers of the Amazon  (Guzkowska et al. , 1990)  and with the potential to derive SWE of rivers and lakes. Since then, various altimetry databases for the global monitoring of lakes and rivers have been developed (Table  3 ).
, 2007) , but connectivity is reduced during the low-water period  (Park, 2020)  as the flows are controlled by the local topography  (Alsdorf et al. , 2007)  and SWE in both environments is not always equivalent  (Alsdorf, 2003). The first large-scale surface water extent mapping from RS was also carried out for the Amazon basin  (Sippel et al. , 1994)  using passive microwave observations. Using several sensors, many estimates and databases have been developed at different spatial and temporal scales (Table  4 ). These include innovative high-resolution mapping of wetlands and flooded vegetation using L-band SAR  (Hess et al. , 2003) , which provided the first estimates of flood extent in the entire Amazon wetlands, ranging between 285 × 10³ and 635 × 10³ km 2 in periods of low (Oct-Dec) and high waters (Apr-Jun), respectively  (Hess et al.