dataesr/scientific-paragraphs-categorization huggingface dataset:
Feature Selection Approaches We study the effectiveness of dimensionality reduction for improving the estimation of forest parameters from the perspective of four approaches: PCA, forward selection, backward selection, and no selection. The evaluation is performed with SVR on 28 features from Sentinel-1, Sentinel-2 and ALOS-PALSAR. Results are presented in Figure  4b. Backward selection was expected to perform best for it maintains groups of features and draws on their synergies. Expectations toward forward selection were lower since the approach adds one feature at a time and is known to dismiss prematurely features that have a beneficial contribution. Yet forward selection performed better and is our recommendation when dealing with small reference dataset. Figure  5  shows how the relative LOO RMSE evolves given the number of features selected by a forward algorithm in order to estimate forest parameters.
Estimation of Aboveground Biomass (AGB) Figure  6a  shows the AGB predictions versus AGB references. The RMSE is 19. 5 tons/ha (28%) and the absolute error is 22. 7%. Figure  6c  shows that the variance of the errors is approximately constant with the increase of the AGB, but the positive or negative orientation of these errors is not random. Indeed, Figure  6c  shows the errors are negative from 100 tons/ha. This is due to signal saturation for AGB estimation. The analysis of the SVR model and the high Cost parameter (C = 500) selected suggest that the model gives high weights to each sample, and has trouble generalizing. This could be improved either by a larger reference dataset or in the future by adding new features that have stronger relationships with the AGB (P-band radar for example).
Stand Age Estimation Age estimation results (Figure  A2  in Appendix 1) are very similar to the DBH estimation results. This was expected since both variables are usually highly correlated in plantation forests. We obtained a RMSE of ~4 years (MAE = 2. 7 years). There is no saturation. The oldest plantations (three samples > 65 years old) are underestimated, but most of the maritime pine plantation are usually clear cut when they get 45-55 years old. The samples above this age could have different management practices, and the age estimation method in reference data (counting the apparent number of annual growth cycles with branch whorls) could introduce some approximation for very high ages. Table  4  and Figure  5  show more features are needed to obtain the best predictions compared to DBH.
Stand Age Estimation Age estimation results (Figure  A2  in Appendix A) are very similar to the DBH estimation results. This was expected since both variables are usually highly correlated in plantation forests. We obtained a RMSE of ~4 years (MAE = 2. 7 years). There is no saturation. The oldest plantations (three samples > 65 years old) are underestimated, but most of the maritime pine plantation are usually clear cut when they get 45-55 years old. The samples above this age could have different management practices, and the age estimation method in reference data (counting the apparent number of annual growth cycles with branch whorls) could introduce some approximation for very high ages. Table  4  and Figure  5  show more features are needed to obtain the best predictions compared to DBH.
Indeed, L-band wavelength (~27 cm) is able to penetrate tree crowns so it is sensible to trunks and large branches, allowing to estimate forest basal area and biomass. The performances of the L-band decrease strongly on DBH, age, density and height. For these forest parameters, Sentinel-1 C-band textural indexes are the best indicators, followed by C-band backscatter, Sentinel-2 and Spot panchromatic textural indexes. Sentinel-1 SAR C-band data is therefore the best pick when using only one data type. C-band (~5 cm wavelength) penetrates the canopy and is sensible to the volume of crown and small branches. As a consequence, it is generally well studied to estimate biomass on young forests below 50 tons/ha, but the signal quickly saturates in older forests. On these coniferous plantations of our study site however, allometric relationships between crown biomass and total tree biomass are strong and Sentinel-1 C-band data is pertinent to estimate forest biomass through the crown structure.
We note that spatial information provided by textural indexes is a good proxy for the estimation of DBH, height, and for AGB as well. We also note that Sentinel-2 spectral indexes and Spot-6 multi-spectral textural indexes show poorer performances if used alone. According to the results of these feature types, Sentinel-1 VH and VV textural indexes (10 m) are the most efficient data for the description of forest spatial structure.
Discussion Previous studies demonstrated the usefulness of open access spaceborne data such as Sentinel-1, Sentinel-2 and ALOS-PALSAR mosaics when applied to different forest types and data combinations. In this paper, we build on these previous efforts in order to harmonize the methods and build a generic approach to produce forest parameters maps. We gather all open access data shown to be useful across different forest types and parameter estimations (SAR L-and C-band, optical indexes, and textural indexes) and develop an automated processing chain that allows to retrieve and spatialize forest key parameters accurately. The method is modular and can be easily tailored to different contexts. The processing chain has been developed with Python (especially scikit-learn library  [66] ) and Orfeo Toolbox (OTB)  [62]  open source software for image processing and machine learning algorithms.
Performance has been evaluated on maritime pine forests within the Landes de Gascogne in Southwest France. We estimate six forest parameters: AGB (28% relative RMSE), BA (27%), DBH (20%), age (17%), density (24%) and height (13%). Height and DBH and age are key variables for forest monitoring, highly related with thinning and harvesting operations. They are predicted with the best accuracy. BA and AGB are important for resources and carbon budget assessment. Their accuracy is lower. One reason can be that the remote sensing data we use are known to saturate for high biomass. Moreover, considering the high spatial resolution of the images, the biomass is more variable than DBH or height from one pixel to another because of tree cover gaps, especially for sowing afforestation. Image segmentation may assist with dealing with such heterogeneous areas.
RF algorithm can be a good option for larger datasets. On these maritime pine forests, the analysis of feature types contributions reveal that the Sentinel-1 textural metrics are the most useful feature type when used alone for the estimation of the ensemble of forest variables. However, L-band is still the better to estimate specifically AGB and BA. With respect to the synergy, the best feature types combination is L-band SAR backscatter with textural indexes from Sentinel-1 or Sentinel-2. Nevertheless, other features can be useful for generalization and it is better to keep a wide range of feature types in order to apply the method efficiently to other forests. In addition, other predictive features can be used when available in the study area. We tested Spot-6 annual mosaics that are freely available on France territory.
In recent decades many forest organizations (e. g. , in France the Office Nationale des Forêts, ONF) have established networks of permanent plots in order to improve forest knowledge and evaluate silvicultural practices. In addition, regular field measurements are performed to assess the financial value of forest stands. These measurements could provide a solid learning database for the method presented in this paper. In return, the possibility of mapping forest structure parameters such as DBH, height and BA with remote sensing data would be useful to obtain quantitative information on stands without measurements, and to improve the distribution and representativeness of permanent plots. The methodology we presented could also be used directly on the geolocated measurements performed by the national forest inventories in order to improve the sampling design and the spatial resolution of their maps.
Funding: This research is part of a PhD funded by The  French spatial agency (CNES)  and the  Agence de l'Environnement et de la Maîtrise de l'Énergie (ADEME). Acknowledgments: Thanks to  INRA  for making available the in situ forest database. We are grateful for data quality to  Sylvia Dayau  ( INRA, UMR ISPA ) and  Bernard Issenhut  ( INRA, Unité expérimentale Forêt Pierroton, 69 route d'Arcachon, 33612 CESTAS, France ). Funding: This research is part of a PhD funded by The  French spatial agency (CNES)  and the  Agence de l'Environnement et de la Maîtrise de l'Énergie (ADEME). Acknowledgments: Thanks to  INRA  for making available the in situ forest database. We are grateful for data quality to  Sylvia Dayau  ( INRA, UMR ISPA ) and  Bernard Issenhut  ( INRA, Unité expérimentale Forêt Pierroton, 69 route d'Arcachon, 33612 CESTAS, France ).
The obtained sequences were edited manually using GeneStudio (TM) Professional Edition 2. 1. 2. 3 and analysed with CLC Main Workbench 7. The sequences were then BLAST searched against the non-redundant nucleotide collections of the NCBI database (https://www. ncbi. nlm. nih. gov/). A COXI haplotype was assigned when match similarity of the BLAST search was greater than 98%.
Genotyping. Samples were genotyped at 15 microsatellite loci developed by Carter, et al. 26. Amplifications were multiplexed in six reactions as follows: (1) ALB38/ALB77, (2) ALB10/ALB53/ALB59, (3) ALB9/ALB14, (4) ALB15/ALB35/ALB44, (5) ALB40/ALB24, and (6) ALB43/ALB19/ALB30). Multiplexed PCR reactions were performed using the Multiplex PCR Kit (Qiagen), as described by Javal, et al. 22. All PCR amplifications were performed on a Veriti 96-Well Fast Thermal Cycler (Applied Biosystems), and PCR products were subsequently denatured by formamide before fragment sizing on ABI PRISM 3500 (Life Technologies, CA, USA). Allele sizes were scored with the GeneMapper v5 software (Applied Biosystems). Ambiguous loci and genotypes that could not be reliably scored were re-amplified and re-analysed.
Data analysis. For population genetics analyses in the four outbreaks in Switzerland (Berikon, Brünisried, Marly, Winterthur), each outbreak was considered as a population. In each ALB population, the number of alleles per SSR locus, the observed and expected heterozygosity  27  , the number of private alleles, the evenness of multilocus genotypes  28  , fixation indices, and the deviation from Hardy-Weinberg equilibrium  29  were estimated using Arlequin 3. 5. 2. 1  30  and the R-package poppr v 2. 3. 0 31. Pair-wise linkage disequilibrium (LD) between loci was tested with the log-likelihood ratio using a Markov chain algorithm (default parameters), as implemented in the web version of Genepop 4. 2  32. The statistical significance of LD was inferred using 1000 permutations and a sequential Bonferroni correction with α = 0.
05. Genetic differentiation among populations was assessed by calculating pairwise F ST -values  29  and corresponding P values (α = 0. 05) with Arlequin 3. 5. 2. 1. Genetic relatedness of the ALB specimens among populations as well as single findings was studied with a multivariate clustering method, i. e. discriminant analysis of principle components (DAPC), implemented in the R-package adegenet  33. First, multilocus genetic data were transformed into principal components (PCs) and the optimal number of PCs was determined with cross-validation  33. Thereafter, ALB groups were predefined according to the origin of the specimens (i. e. , the four outbreaks and the single findings). Samples were then plotted along the first two discriminant functions of the analysed PCs. The population genetic structure of the four outbreaks was analysed with the Bayesian model-based cluster analysis, as implemented in STRUCTURE v 2.
The demographic scenario that best explained the observed genetic diversity in outbreaks was inferred from two analysis steps (for details see Supplementary). First, the most likely sequences of demographic events and defined topology of ALB populations' spread were tested. In this step, we used a broad frame of prior population parameters to compute simulated data-sets. Then, considering information from the field observations we restricted the first event of the ALB arriving to 10 generations ago and defined the time of each demographic event in a likelihood scenario. In both steps, the ABC analysis was conducted following Cornuet, et al. 41  and included the following steps: (1) assume realistic competing scenarios considering structure, F ST ratio between sampled populations, and field observations; (2) simulate 1 × 10 6 pseudo-observed datasets (PODs) for each scenario and compute correspondent summary statistics; (3) evaluate posterior probabilities of each scenario on 1% PODs with the closest summary statistic to the observed dataset and identify the best scenario in 95% confidence interval; (4) assess the confidence level of the chosen scenario as the proportion of times that this scenario was falsely rejected (type-I error) or accepted (type-II error); (  5 ) evaluate the goodness-of-fit of the selected scenario to the data.
Participants One hundred and fourteen depressed patients were recruited from routine care units in the psychiatric university hospital of Rennes between November 2014 and January 2017 and were enrolled in a naturalistic prospective cohort study. The study was approved by an ethic committee and is registered in www. clinicaltrial. gov (NCT02286024), written informed consents were obtained from all subjects. The study was proposed to patients suffering from a Mood Depressive Episode (MDE) under DSM-5 criteria with or without personal history of MDD (unipolar or bipolar subtype). Exclusion criteria included other Axis-I disorders (except for obsessive-compulsive disorder and four anxious comorbidities such as posttraumatic stress disorder, social phobia, generalized anxiety disorder or panic disorder), and psychotic symptoms, which were explored using the Mini-International Neuropsychiatric Interview  (Lecrubier et al.
MRI acquisition Patients and healthy subjects were scanned on a 3 T whole body Siemens MR scanner (Magnetom Verio, Siemens Healthcare, Erlangen, Germany) with a 32-channel head coil. The 3D T1-weighted image was acquired covering the whole brain (176 sagittal slices) with TR = 1. 9 s, TE = 2. 26 ms, flip angle = 9°, in-plane resolution = 2 mm × 2 mm, FOV = 256 mm × 256 mm and thickness/gap = 1. 0/0 mm). DWI data were gathered on 60 slices using an interleaved slice acquisition, slice thickness of 2 mm, no gap, in-plane resolution = 1 mm × 1 mm and in a 256 mm × 256 mm field of view. The acquisition matrix was 128 × 128, the reconstruction matrix was 128 × 128, using 30 directions and a b-value of 1000 s/mm 2. TR/ TE = 11,000/99 ms, flip angle was 90°, pixel bandwidth was 1698 Hz, and the imaging frequency was 128 MHz.
Image processing Preprocessing of diffusion images was mainly performed using the open source medical image processing toolbox Anima (https://github. com/Inria-Visages/Anima-Public/wiki). Diffusion images were corrected for eddy current-induced image distortion using a blockmatching distortion correction method ensuring an opposite symmetric transformation  (Hedouin et al. , 2017). Then, rigid realignment was then performed to compensate for subject motion and ensure voxel-tovoxel correspondence across gradients. Then, denoising step using blockwise non-local means filtering was applied  (Coupé et al. , 2008). Skull stripping was performed using an atlas-registration-based method. More precisely, the structural image of each patient was transformed to the atlas image, using the linear and non-linear block-matching algorithms  (Commowick et al.
, 2012b; Ourselin et al. , 2000). Then the structural image was non-linearly transformed to the MNI template space, using the block-matching  (Commowick et al. , 2012b; Ourselin et al. , 2000). Then, we applied those transformations to the other diffusion metric maps (ADC, AD and RD maps) to normalize them to the MNI space. Next, a cross-subject mean FA image was computed to generate a WM tract skeleton, which was thresholded to FA > 0. 3 to confine the statistical analysis within the major WM tracts  (Du et al. , 2017). Then, this map was used to mask the FA, apparent diffusion coefficient (ADC), axial diffusivity (AD) and radial diffusivity (RD) maps. The preprocessing pipeline is available online on github https://github. com/Inria-Visages/Anima-Scripts-Public (in diffusion/animaDiffusionImagePreprocessing.
Acknowledgments MRI data acquisition was supported by the  Neurinfo   MRI research facility  from the  University of Rennes I. Neurinfo  is granted by the  European Union (FEDER) , the  French State , the  Brittany Council ,  Rennes Metropole ,  Inria, Inserm  and the  University Hospital of Rennes. This work has been funded by  Institut des Neurosciences Cliniques de Rennes (INCR). The authors thank  Mr. Stéphane Brousse  and  Mr. Jacques Soulabaille  for their involvement in the conduct of the study.
Nine sedimentary dams were built in the lowest part of the basin in the 1970s to enhance sediment deposition and protect the city of Nice  (Anthony and Julian, 1999). The climate in the Var basin is mostly controlled by elevation and is thus highly variable, from a Mediterranean climate in the south to a mountainous climate in the north. Between 1981 and 2010, the mean annual temperature and mean annual precipitation recorded at Nice (2 m above 25 sea level; Fig. 1 ) were 16 °C and 733 mm yr -1 , respectively, and those at Guillaumes (790 m above sea level; Fig. 1 ) were 10. 7 °C and 910 mm yr -1 , respectively (Meteo France database; https://donneespubliques. meteofrance. fr).
7 g cm -3 ) and  is the 10 Be concentration measured in the fluvial sediments collected at the basin outlet. We calculated basin-averaged 10 Be production rates for each production pathway and each studied subbasin using the 5 Basinga GIS tool  (Charreau et al. , 2019) , based on the Lal-Stone time-dependent scaling model  (Lal, 1991; Nishiizumi et al. , 1989; Stone, 2000) , and using up-to-date worldwide production rates computed by the Cosmic Ray Exposure program online calculator (https://crep. otelo. univ-lorraine. fr/; Martin et al. , 2017). We accounted for basin topography using a cellby-cell approach based on the 5 m resolution digital elevation model "RGE ALTI" produced by the French Institut National de l'Information Géographique et Forestière. Denudation rates were corrected for paleomagnetic variations that occurred 10 during the integration time and topographic shielding  (Charreau et al.
Data availability A raw data table including all samples is available in supplement. 20 7 Authors contributions AM, PHB and JC designed the study. AM, PHB, JC and CP collected the samples for analysis. AM prepared the samples for 10 Be analysis and the ASTER Team measured the 10 Be/ 9 Be ratios using the French Service National AMS ASTER. AM, 25 PHB, JC and SM analyzed the data. AM wrote the initial manuscript and all authors commented and contributed to the final version.
Acknowledgements This research was funded by the ANR JC EroMed project (N° ANR-17-CE01-0011-01; Principal Investigator P. -H. Blard). 35 This work is part of the PhD thesis of Apolline Mariotti, whose scholarship was jointly supported by the CNRS and the Région Grand Est. Fruitful discussions with Samuel Toucanne, Stephan Jorry, Lucille Bonneau and Vincent Godard benefited the design of the study and interpretation of the results. We are grateful to Emmanuel Davy and Abdellah Zitouni for their help with quartz separation at CRPG, and to Laëticia Léanni and Régis Braucher for their expertise in 10 Be wet chemistry at LN2C/CEREGE. Robert Dennen is acknowledged for proofreading and improving the overall readability of the 40 manuscript. " " " " " " " " " " " " " " " " " "     1 ). White polygons denote the absence of data.
The aerosol module in CHIMERE includes different chemical and physical processes such as gas-particle partitioning, coagulation, nucleation, condensation, and dry and wet deposition. The chemical speciation contains EC (elemental carbon), sulfate, nitrate, ammonium, SOA and SVOC species, dust, salt, and PPM (primary particulate matter other than that mentioned above). More information on the SOA scheme will be provided in the next section. The simulation domain covers all of Europe with a resolution of 0. 44 • ; the domains used in all the simulations are all the same (the domain approximately covers 30-70 • N and 40 • W-60 • E).
OA schemes used for the simulations The CHIMERE model has three SOA simulation schemes with different levels of complexity, all based on a molecular single-step oxidation scheme. In our base simulations, the medium complexity scheme is used  (Bessagnet et al. , 2008). In this scheme, lumped VOCs can react and form classes of organics with reduced volatility, i. e. , SVOCs. Once formed, the model distributes these species between the gaseous and particulate phases according to the mixing theory of  Pankow (1987). The yields for the formation of SOA are taken from  Odum et al. (1997) ,  Griffin et al. (1999) , and  Pun and Seigneur (2007). This scheme is referred to as the SOA2p scheme hereafter. A large database of historic and future simulations exists for this scheme, for three RCPs (RCP2.
(2015)  are used without any change. Both the standard VBS without biogenic aging (referred to as SOAvbs scheme hereafter) and the modified VBS including fragmentation and formation of nonvolatile aerosol (referred to as SOAmod scheme hereafter) schemes are presented in more detail and compared to experimental data in the western Mediterranean area in  Cholakian et al. (2018). In the aforementioned work, it was concluded that these two schemes can reproduce the levels of concentration of organic aerosols in the Mediterranean Basin successfully in regard to concentration of OA, while the oxidation state and fossil and non-fossil repartition is better represented in SOAmod.
Choice of years The SOAvbs and the SOAmod schemes are both numerically very resource-consuming; therefore, only 10 years of simulations for each scheme were performed. In order to choose the appropriate years for the simulation, existing long-term sets of simulations were used, containing 30 years of historic simulations  and 70 years of future scenarios (2031-2100). We address results for BSOA, as it makes the major contribution to OA during summer (between 40 % and 78 % for different schemes in the historic scenario, according to our simulation results for the historic period with differences schemes; not shown in figures). The simulations were performed using the previous version of CHIMERE (CHIMERE 2013b;  Menut et al. , 2013) , the SOA2p scheme and the RCP8. 5 scenario. This dataset was used to choose five years of simulations in the historical and future periods each, with the aim of maximizing both the temperature and SOA differences between historic and future scenarios.
, 2014). The observations are mostly accessed from the EBAS database (http://ebas. nilu. no/, last access: 14 June 2019;  Tørseth et al. , 2012). The used measurements are mostly PM 2. 5 and, in some cases, PM 1. For each type of measurement (PM 2. 5 or PM 1 ), the corresponding fraction from the simulations was used. In some cases, data were pro- Bear in mind that for some of these stations the available data cover a shorter period than 1 year, or they present weekly measurements rather than daily observations. The list of stations with information about each station and the type of measurement is provided in Appendix A. Results of these comparisons are shown in Fig. 3 , and the statistical information is shown in Table  1. Regarding the concentration of OA, the modified VBS scheme shows a stronger bias (-0.