dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank the  CTF team  for the tests ATA02 and the data regarding coils, in particular  Roser Vallcorba  for providing the data. The authors would like to thank  Manfred Wanner  for his advices and fruitful discussions regarding the interpretations of the present results. This work was undertaken under the Broader Approach Agreement between the  European Atomic Energy Community  and the  Government of Japan. The views and opinions expressed herein do not necessarily state or reflect those of the Parties to this Agreement.
, 2018; Potts and Willmer, 1997 ). Yet, despite recent regulation in favor of grasslands, agriculture intensification, abandonment and urbanization generate a decrease of both grasslands surface area and plant diversity  (O'Mara, 2012; Newbold et al. , 2016)  leading to a loss of biodiversity (plants, insects, birds) and its related ecosystem services. Therefore, there is an urgent need to monitor grasslands over large extents in order to assess their current state in terms of area covered but also to estimate their plant diversity and their carrying capacity for insect populations. While the area information can be extracted from land cover databases (e. g. CORINE Land Cover  (Heymann, 1994) ) diversity and carrying capacity data are scarcely available. Though, spatial location, extent and quality of grasslands are crucial for their conservation per se but also to produce maps of related ecosystem services in agricultural areas.
Because plant communities differ in their temporal behavior, the phenological diversity measured by these sensors could be used as a proxy to estimate the plant diversity. Second, they provide complementary information about the grassland cover: optical data are influenced by chemical composition of the elements while radar data are sensitive to the geometric configuration of the elements  (Joshi et al. , 2016). Then, being available for free, they can be used to process large areas and complement field surveys at a reduced cost. Yet, recent works using Sentinel-2 images are still performed on small areas (a few square kilometers)  (Rapinel et al. , 2019). Although interesting, studies over such small areas do not have to cope with a high diversity of grasslands in terms of composition neither with the spatial variability of meteorological, topographical conditions and farmers pratices.
The climate is sub-Atlantic with sub-Mediterranean and mountain influences (mean annual temperature, 12. 5 • C; mean annual precipitation, 750 mm). Botanical surveys were conducted in 83 plots from distinct grasslands between April and May 2018 after the flowering and before the mowing. The average distance between two plots was 13. 4 km with a standard deviation of 9. 1 km and the minimum and the maximum distances were 0. 08 km and 44. 37 km, respectively. Each plot was composed of 5 quadrats of 1 square meter area (Figure  2 ). The center of each quadrat corresponded to the theoretical center of a Sentinel-2 pixel. Note that since the absolute geolocation is below 11 meter at 95. 5% confidence (ESA, 2019), a possible mis-registration between Sentinel-2 data and GNSS can be observed for few pixels at some dates.
Optical data Four Sentinel-2 tiles of Level 2A were downloaded from the Theia Land Data Centre 1 , corresponding to all available acquisitions between August 26, 2017 and December 03, 2018 2. Only the bands natively at 10 m/pixel were used in the experiments (blue, green, red and nearinfrared). Surface reflectance time series were produced using the MAJA (Multi-sensor atmospheric correction and cloud screening-ATCOR Joint Algorithm) processing chain developed by the CNES-CESBIO and DLR. It includes orthorectification, atmospheric correction, clouds and shadows detection  (Baetens et al. , 2019). All the acquisitions were re-sampled and gap-filled (to handle clouds and shadows) onto the same set of dates (every 10 days, starting from 2017-08-29 and ending in 2018-12-02), as proceeded in  Inglada et al.
Radar data Sentinel-1 Level-1 GRD (Ground Range Detected) time series recorded in interferometric wide swath mode were used. Four orbits corresponding to the Sentinel-2 extent were downloaded from the French Sentinel collaborative ground segment 3 , corresponding to the same temporal period as Sentinel-2 time series. GRD time series were calibrated to γ 0 , orthorectified and filtered using a multitemporal spatial filter  (Quegan and Yu, 2001). The parameters of the spatio-temporal filter were a spatial window of size 5×5 pixels and all the dates were used for the temporal filters. VV and VH polarizations were processed as well as the ratio VV/VH. The features were re-sampled onto the same set of dates (every 10 dates starting from 2017-08-27 to  2018-11-30). Figure  4  presents the reconstructed γ 0 (VV) polarization (in blue) for descending orbits for the same four plots.
Regression techniques Five conventional machine learning algorithms were used in the experiments with different set-up  (Lary et al. , 2016) : Linear regression (with ridge and lasso regularization)  (Hastie et al. , 2009, Chap. 3), K-Nearest Neighbors (K-NN)  (Hastie et al. , 2009, Chap. 13 ), Kernel Ridge Regression (KRR)  (Murphy, 2013, Chap. 14) , Random Forest (RF)  (Hastie et al. , 2009, Chap. 15)  and Gaussian Process (GP)  (Rasmussen and Williams, 2006). K-NN, KRR, RF and GP are non-parametric and non-linear algorithms that have been used successfully in many remote sensing applications, see for instance  Maxwell et al. (2018). In all the experiments, the Scikit-learn python library was used  (Pedregosa et al. , 2011). Hyperparameters of ridge, lasso, K-NN, RF and KR methods were fitted through crossvalidation  (Hastie et al.
Perspectives Perspectives for this work are two-fold. First, field work for different grassland areas (mountains, wetland. ) should be conducted to enrich the learning database and to assess the robustness of the prediction at the country scale. Second, different stratification strategies should be investigated, for instance according to their types or their eco-climatic conditions. Third, additional data set, such as DEM or surface temperature, should be used jointly for the prediction in order to capture more information about grasslands. This would require specific methodological developments to properly use such a heterogeneous data set.
Acknowledgement This work was supported by the  SEBIOREF (National Institute for Agronomic Research and Occitanie Région)  project which funded part of the field work. Catherine Bataillon ,  Sylvie Ladet ,  François Calatayud ,  Wilfried Heintz ,  Xavier Henry  and  Jérôme Willm  are gratefully acknowledged for the field work in Spring 2018. We also acknowledge farmers for allowing full access to their grasslands. Mathieu Fauvel is thankful to  Jordi Inglada ,  Vincent Thierion  and  Arthur Vincent  for their support and help with iota 2 software. The authors would also like to thank the reviewers and the handling editor whose comments and suggestions improved this paper.
II. NUMERICAL MODELLING To study the correlation between the electrons and positrons emittance at the rear of the converter target, a series of Monte-Carlo simulations using the scattering code FLUKA  34, 35  have been performed. We simulate 10 7 mono-energetic electrons contained in a pencil-like beam with different energies: 0. 15, 0. 5, 1, 2, and 5 GeV. These interact with a 10mm-thick Pb converter (corresponding to approximately 1. 8 radiation lengths), where the target thickness has been chosen so to maximise the positron yield at the rear surface  6. In principle, the divergence and source size of the primary electron beam should be included, since they might affect the spatial properties of the particles escaping the converter target. However, as shown later, the geometrical emittance of the positrons escaping the target is of the order of a few microns, as dictated by the spread induced by the quantum electrodynamic cascade inside the converter.
8 mW/cm 2. Typically, the illumination time was 30 s and the dark adaptation time was 50 s, except for the potential dependence measurements for which the dark adaptation time was extended to 200 s after each change of the applied potential. For the latter measurements, the value of photocurrent was read out as a peak value of the photocurrent after baseline dark current subtraction (see supplementary information for the detailed description of the procedure). n PSI = A 679 nm N A d 2 679 nm m When stated that data were obtained at open circuit potential (OCP), the OCP was measured by the potentiostat in the darkness and then applied to WE to keep zero dark current between WE and CE. Measurements of the photocurrent action spectra were carried out with a photoelectric spectrometer (Instytut Fotonowy) and the same spectroelectrochemical cell as used for the chronoamperometric measurements.
Cathodic photocurrent at open circuit potential Figure  3a  shows typical chronoamperometric data recorded for the PSI-FTO WE in the spectroelectrochemical cell shown in Fig. 2  yielding the photocurrent of ~ 500 nA/ cm 2. These data were recorded after standard single electrodeposition step in which ~ 5 monolayers of PSI were formed. In a control experiment (not shown), we checked that the photocurrent increases proportionally to the number of the electrodeposition steps. The data were recorded for OCP = + 100 mV, which was the potential applied to WE blocking any dark current but not photocurrent. The value of OCP is the redox potential of the mixture of electrolyte components used and thus is dictated by the concentration, initial redox states and midpoint potentials (E 0 ) of DCPIP (added to the solution in the oxidized form) and ascorbate (added in the reduced form).
CMEM soil module In the soil module, the dielectric mixing model is used to compute the soil dielectric constant depending on the microwave frequency, soil moisture and temperature and soil texture. Three parameterizations are implemented in CMEM to infer the soil dielectric constant. The Dobson model is valid for frequency in a range of 1 GHz to 20 GHz  (Dobson et al. , 1985). The Mironov  (Mironov et al. , 2004)  and the  Wang and Schmugge (1980)  models are valid for frequencies between 1 GHz and 10 GHz. Results from de  Rosnay et al. (2009a)  over West Africa showed that the Wang and Schmugge model and the Mironov model perform better than the Dobson model at C-band. They account for the effect of bound soil water and they are more suitable for a large range of frequencies. The Mironov parameterisation has been widely used for Lband applications at the field scale  (Mialon et al.
CMEM vegetation module The vegetation layer is represented in CMEM following the approach. As formulated in Equation (  2 ), vegetation contributes in several ways to the measured signal at the top of the atmosphere. It attenuates the soil emission, the downward atmospheric emission and the upward atmospheric emission after it was reflected on the soil surface, and it has a direct contribution to the signal expressed as: = T T exp (1 ) (1 ( )) Bveg p c p veg p , , (6) where T c is the canopy temperature and p is the single scattering al- bedo at polarisation p. CMEM includes a choice of four parameterisations to account for the vegetation effect on the signal. They differ in the approach used to compute the vegetation optical depth depending on the Leaf Area Index and vegetation water content as described in details in a number of papers including for example  (de Rosnay et al.
CMEM snow and atmospheric modules In the case of presence of snow, CMEM accounts for a snow layer as described in  Holmes et al. (2008)  with the snow reflectivity computed using the single layer version of the Helsinki University of Technology (HUT) model  (Pulliainen et al. , 1999). In this study the snow covered areas are filtered out in the quality control as described in the next subsection. In the atmospheric module of CMEM, the atmosphere optical thickness atm p , is computed following the parameterisation developed by  Pellarin et al. (2003).
Summary and conclusion This paper presented the SMOS forward modelling activities conducted at ECMWF to use the SMOS TB data. The first step was to develop and configure the observation operator CMEM. We evaluated different combinations of CMEM parameterisations against SMOS observations and to define CMEM's configuration for SMOS monitoring and data assimilation activities at ECMWF. To identify the best configuration of CMEM, 36 numerical experiments, using different combinations of three dielectric models, three vegetation opacity models and four roughness models, were conducted at 80 km resolution and at 40°incidence angle for 2010-2011. Experiments were conducted using ERA-Interim conditions as input of the land surface model H-TESSEL and the forward model CMEM. Results were evaluated against SMOS TB observations at both polarisations.
They showed that simulated TBs are most sensitive to the soil roughness models, with less sensitivity to the In a second step, we presented and evaluated the SMOS bias correction approach developed to use SMOS TB measurements for data assimilation purpose as investigated by  Muñoz-Sabater et al. (2019). The paper described a multi-angular multi-polarised bias correction method based on a linear rescaling computed at the monthly scale using SMOS observations and ECMWF-CMEM re-analysed (ERA-Interim) TBs for a four year period  (2010) (2011) (2012) (2013). Experiments were conducted at 40 km resolution with and without bias correction and results were compared to the SMOS observations at global scale and at regional scale. Results showed that the seasonal multi-angular multi-polarisation bias correction approach reduces global RMSE to 7.
These results nevertheless clearly show an overall substantial quality improvement SMOS TB product at both polarisations between 2010 and 2014 and between 2015 and 2016. The forward modelling results and bias correction results presented in this paper demonstrate the relevance of the SMOS observations for NWP applications. Long term monitoring results also open perspectives for SMOS and SMAP brightness temperatures data assimilation studies for environmental systems monitoring, prediction and long term reanalyses.
Materials and Methods In the method sections below we describe the implementation of a new tree rooting scheme in LPJmL4. 0 (Sect. 2. 2) where maximum rooting depth is constrained by a recent map on maximum soil/sediment thickness (Sect. 2. 3). We apply the model to several historical climate input data (Sect. 2. 7) with details of the simulation protocol described in Sect. 2. 9. The Evaluation of the new model version is described in Sect. 2. 10. All data processing and statistical analysis described in the methods sections were performed with the commercial software MATLAB® (MATLAB and Statistics Toolbox Release 2012b; The MathWorks, Inc. , Natick, MA, USA).
, 2016). We regard increasing the maximum mortality rate as a step into the right direction as its value can eventually be set close to 100% when model development progresses. (Weedon et al. , 2011)  and the WFDEI data set  (Weedon et al. , 2014)  as used in the ISIMIP project (https://www. isimip. org/gettingstarted/input-data-bias-correction/details/5/). This input data set is called WATCH+WFDEI hereafter.
Continental scale gridded evapotranspiration products and selection of regions To evaluate the ET over large regions and during a long period (1981-2010), we use three global gridded datasets: Global Land Data Assimilation System Version 2  (Rodell et al. , 2004) , ERA-Interim/Land (ERAI-L,  Balsamo et al. , 2015)  and Global Land Evaporation Amsterdam Model v3. 2 (GLEAM,  Miralles et al. , 2011; Martens et al. , 2017). GLDAS and ERAI-L are land-reanalysis products, meaning that they are land surface models forced with meteorological data that has been corrected with observations to give better estimates of land surface variables. The selection of these two products is based on the study of  Sörensson and Ruscica (2018) , who found that they have a better performance over South America than other reanalysis and satellite-based ET products.
GLDAS uses the land surface model Noah  (Ek et al. , 2003)  forced by Princeton meteorological dataset version 2. 2  (Sheffield et al. , 2006). land surface model HTESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land,  Balsamo et al. , 2009)  forced by ERA-Interim atmospheric data with a GPCP based correction of monthly precipitation. The soil depth of ERAI-L is 2. 89 m, the model uses four soil layers and vegetation data from ECOCLIMAP  (Masson et al. , 2003). GLEAM uses the Priestley-Taylor equation to estimate the potential ET and a set of algorithms with meteorological and vegetation satellite data as input to calculate the actual ET. The version used here, GLEAMv3. 2a (Martens et al. , 2017,  downloaded from https://www. gleam. eu/#downloads) uses precipitation input from MSWEP v1.
MCWD is an indicator of seasonal water stress (see section 2. 5. 3). The original spatio-temporal resolution of GLDAS and GLEAM is 0. 25º x 0. 25º while for ERAI-L it is 0. 75º x 0. 75º. Monthly time series were calculated from daily values for the three datasets. Hereafter, we use the short names GLDAS, ERAI-L and GLEAM for the described reference datasets.
Spatial distribution of vegetation types To evaluate the regional distribution of simulated biome types in all LPJmL versions we compare our results to satellitederived vegetation composition maps from ESA Land cover CCI V2. 0. 7  (Li et al. , 2018)  reclassified to the PFTs of LPJmL from  Forkel et al. (2014). In this dataset PFT dominance is indicated by foliage projected cover (FPC) which is also a standard output variable of all LPJmL models enabling a direct comparison of model results.
Continental Evapotranspiration Results of regional ET are in line with results of site-specific ET. The climatological clusters within the Amazon region which undergo the strongest dry season (EQ E and SAMz) show the largest differences between LPJmL4. 0-VR and the other 2 models and also here LPJmL4. 0-VR shows a higher agreement with validation data (Fig. 11c , d and Table  6 ). Improvement is largest for EQ E where NME and r² show values of 0. 62 and 0. 91, respectively, whereas the other 2 models show values of NME >= 1. 92 and r² <= 0. 21. As expected in NSA and EQ W model differences become less pronounced as annual precipitation deficits are lower and deep rooting systems play a lesser role, but still there is noticeable improvement e. g. in NSA between January and April, where monthly precipitation is lower than during the rest of the year.
AGB at specific sites For site specific comparisons of simulated and observed AGB we calculated 3 indicators, 1) the ratio of means (  ⁄ ) as a measure for the agreement of pattern average, 2) the ratio of standard deviations of large-scale AGB patterns (   ,  ⁄ ) as a measure for the agreement of pattern amplitude (the differences between grid cells), and 3) the modified 'large-scale' Pearson correlation coefficient (  ) as a measure for the agreement of large-scale pattern shape (the location of maxima and minima). (2015). We find that for all climate datasets, LPJmL4. 0 tends to overestimate and LPJmL4. 0-VR-base tends to underestimate average AGB across forest plots in the Amazon region. Except for GLDAS, average AGB from LPJmL4. 0-VR lies between these two cases, showing the closest match with average AGB derived from forest plots.
However, uncertainties in average AGB from forest plots is quite large (as indicated in spread of violine) so that for all but two cases (LPJmL4. 0-VR-base with GSWP3 and WATCH+WFDEI)   ⁄ = 1 falls within the 95 % confidence interval of   ⁄. With regard to the pattern's amplitude (), we find that for all climate datasets all model versions tend to overestimate AGB differences across the Amazon, but only for LPJmL4. 0 with GSWP3 and WATCH+WFDEI unity is outside the 95 % confidence interval of    ,  ⁄. In other words the spatial difference between grid cell biomass is generally larger than In summary, we conclude that LPJmL4. 0-VR reproduces the 'observed' large scale AGB pattern in the Amazon in all three relevant aspects (pattern mean, amplitude and shape) better than either of the two other model versions.
12b ), MDRU values might be overestimated. 7) LPJmL4. 0-VR does account for tropical tree PFTs only. Bush and shrub PFT types which might be evergreen and gain access to deep water while stem size remains relatively small  (Oliveira et al. , 2005)  are not accounted for. Implementing more general PFTs into LPJmL4. 0-VR is in the focus of future studies. For this study we checked the data availability on maximum rooting depth across South America in the TRY database  (Kattge et al. , 2020; data downloaded September 2019). As it is also shown in  Fan et al. (2017; Fig. 2)  we found the number of sites within the TRY data base where maximum rooting depth has been measured in South America to be very low. Moreover, the number of data entries per site appeared very small, where 33 TRY sites falling within our study area showed a mean of ~9 and a median of 6 data entries, while 15 sites showed <=5 data entries.
observations imply. Nevertheless, pattern amplitude decreases with increasing model complexity (from LPJmL4. 0 over https://doi. org/10. 5194/bg-2020-97 Preprint. Discussion started: 27 March 2020 c Author(s) 2020. CC BY 4. 0 License. LPJmL4. 0-VR-base to LPJmL4. 0-VR) so that for LPJmL4. 0-VR unity falls within the interquartile range of    ,  ⁄ for all climate datasets. Note, however, that for GLDAS median    ,  ⁄ for LPJmL4. 0-VR is slightly larger than for LPJmL4. 0-VR-base but the 25 % percentile is lower for LPJmL4. 0-VR due to the wider uncertainty distribution. Evaluation of the shape of the large-scale average AGB pattern shows that median   increases with increasing model complexity. In other words LPJmL4. 0-VR matches large scale maxima and minima of biomass across the Amazon forests best.
Acknowledgements BS and KT acknowledge funding from the BMBF-and Belmont Forum-funded project "CLIMAX: Climate services through knowledge co-production: A Euro-South American initiative for strengthening societal adaptation response to extreme events", FKZ 01LP1610A. MD is funded by the DFG/FAPESP within the IRTG 1740/TRP 2015/50122-0. MH is supported by a grant from Instituto Serrapilheira/Serra 1709-18983. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices.
, 2008). Being the toxicological database of this group is limited and no reports on adverse effects in humans so far, EFSA established provisional TEFs value of 1 for PTX-1, PTX-2, PTX-3, PTX-4,  PTX-6 and PTX-11 (European Food Safety Authority, 2009). Cyclic imines (CIs) are a heterogenic group of macrocyclic compounds having imine and ester groups and spiro unions (Fig. 1 )  (European Food Safety Authority, 2010; Molgó et al. , 2014). The SPXs are neurotoxic and their target is nicotinic and muscarinic acetylcholine receptors (mAChR and nAChR, respectively) in the central and peripheral nervous system and at the neuromuscular junction  (Meilert and Brimble, 2006; Molgó et al. , 2014). These toxins were discovered in the 90s during a routine monitorization in Canada and New Zealand, but in subsequent years CIs have been also detected in European waters  (Amzil et al.