dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on a modified Century model  38, 39  , it stratifies the biomass carbon stock into four pools (leaf, stem, coarse root, and fine root pools) and the soil carbon stock into nine pools (surface structural litter, surface metabolic litter, soil structural litter, soil metabolic litter, coarse woody litter, surface microbe, soil microbe, slow, and passive carbon pools). These carbon pools are initialized in the following way. The model first calculates NPP in 1901 using N deposition and CO 2 concentration in 1901, a random year of climate data selected in the period from 1901 to 1910, seasonally variable LAI averaged over the 1982-1986 period, and default C:N ratios for all carbon pools. The nine soil and four biomass carbon pools are then estimated under the assumption that the carbon cycle of terrestrial ecosystems was in dynamic equilibrium in 1901.
NEP of each modeling grid equals GPP-AR-HR. LAI data. LAI is an input into the BEPS model for the simulation of the carbon flux. Three LAI time series, GLOBMAP-V2, GLASS, and LAI3g are used in this study and are shown in Supplementary Fig. 1  in comparison with other LAI time series. The GLOBMAP _V2 product is the basis for our simulations, whereas GLASS and LAI3g are used to assess uncertainties in carbon sink estimation due to the choice of LAI products. GLOBMAP_v2 over the period from 1981 to 2016 was generated through fusing LAI inverted from MODIS reflectance data with AVHRR GIMMS NDVI data. LAI from 2001 to 2016 was first derived from the MOD09A1 C6 land surface reflectance and the associated illumination and view angles based on the GLOBCARBON LAI algorithm  19, 41  , which was developed on the basis of the 4-Scale geometric optical model  42.
Depending on the strength of quality control, different LAI products could show different interannual variations, although they are consistent in their increasing trends (Supplementary Table  1 ). LAI data have been assimilated into diagnostic ecosystem models to optimize plant carbon allocation, stock, and residence time, as well as carbon-use efficiency  16, 45. In our study, we similarly optimized some of these parameters through a spin-up procedure and also used LAI data to calculate the long-term global carbon cycle. Meteorological data. Meteorological data required to force the BEPS model include daily maximum and minimum temperatures, downward solar radiation, relative humidity, and precipitation. These data are interpolated from the 0. 5°× 0. 5°CRUNCEP V8. 0 dataset, which is a combination of CRU monthly climatology and 6 hourly NCEP reanalysis meteorological data  46.
Daily maximum and minimum temperatures, downward solar radiation, and precipitation are directly retrieved from the CRUNCEP V8. 0 dataset. Relative humidity is calculated from temperatures, specific humidity, and pressure from the CRUNCEP V8. 0 dataset. The 0. 5°× 0. 5°meteorological data are interpolated into 0. 072727°× 0. 072727°r esolution using a bilinear interpolation method. These data are the same as those used by the TRENDY models. Soil data. Fractions of clay, silt, and sand are retrieved from the harmonized global soil database (http://www. fao. org/nr/lman/abst/lman_080701_en. htm) and are used to determine soil physical parameters, including wilting point, field capacity, porosity, hydrological conductance, exponent of the moisture release equation, and so on. Nitrogen deposition data.
The yearly global nitrogen deposition data at 0. 5°× 0. 5°r esolution over the period from 1960 to 2009 are estimated from tropospheric NO 2 column density retrieved from Global Ozone Monitoring Experiment and Scanning Imaging Absorption Spectrometer for Atmospheric Cartography sensors, meteorological data, and NOx emission inventory data  47. For the years from 2010 to 2016, nitrogen data are extrapolated using the estimated nitrogen data over the period from 2000 to 2009. For the period from 1901 to 1959, nitrogen data are extrapolated based on the change rates of nitrogen deposition over the period from 1960 to 1969. The 0. 5°× 0. 5°nitrogen deposition data are interpolated into the 0. 072727°× 0. 072727°resolution using a bilinear interpolation method. The N deposition dataset used in this study is compared with that used by TRENDY models (Supplementary Fig.
2 ). These two datasets are similar before 1990, as both are based on measurements, but the increasing trends after 1990 are different, because the data used by the models are based on linear extrapolation from 1990 to 2050, at which the nitrogen deposition is estimated based on projected anthropogenic sources and other assumptions  48  , whereas satellite measurements from 2000 to 2009 are used in our dataset  47  and could follow the realistic trend more closely than the linearly extrapolated trend used by the models. Over the 1981-2016 period, the total N deposition is 301 Tg N in our study, whereas it is 403 Tg N in TRENDY. The difference could be due to the overall decrease in N deposition in North America and other regions in this period. Uncertainty assessment. The uncertainty of model results shown in Figs.
Acknowledgements This study is funded by the  National Key R&D Program of China  ( 2016YFA0600202 ) and in part by the  National Natural Science Foundation of China  ( 41671343 ) and by the  Fundamental Research Funds for the Central Universities. P. C. acknowledges support from the  European Research Council  Synergy project SyG-2013-610028  IMBALANCE-P  and the  ANR CLAND Convergence Institute.
Then, the calibrated RF model was applied to remote sensing data to produce an AGB map of Gabon. To improve the accuracy of the remote sensing-derived AGB map, the GLAS LiDAR data were considered. First, the GLAS data were calibrated to AGB, providing a spatially distributed (GLAS footprints geolocation) GLAS-derived AGB (surrogate AGB data). Second, the spatial dependency between residuals (RS-derived AGB map e surrogate AGB data at each GLAS footprint location) was modeled by mean of a semivariogram. Then, the semivariogram parameters were used to perform a regression kriging (RK) interpolation of residuals, providing a residual map. Finally, the residual map was added to the RS-derived AGB map. A description of the study area and the datasets is given in Section 2. Section 3 presents the methodology.
Reference AGB maps In this study, 50-m AGB maps for four regions of the AfriSAR campaign (namely, Lop e, Mabounie, Mondah, and Rabi  (Hajnsek et al. , 2016) ) were used as the reference AGB dataset to calibrate and validate the result of our AGB mapping (Fig. 1 ). These maps (total area of 340 km 2 ) were generated by Labri ere et al. (2018) using small-footprint airborne LiDAR data (acquired in 2007 for Manounie, 2011 for Mondah and 2015 for both Lop e and Rabi). A simple power-law model relating field-based AGB to the median canopy height (LiDAR-derived metrics that performed better than mean canopy height) was built. The calibrated model was then applied to derive the AGB maps for the four regions. For the four Gabonese regions, the values of the RMSE on AGB estimates at 100-m and 50-m spatial resolutions were 47.
ALOS/PALSAR data A 25-m mosaic SAR image in the L-band acquired by the Phased Array Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) was used. The mosaic SAR is dual-polarization (HH and VH). Only the VH polarization was used since it is more sensitive to forest layers than HH  (Minh et al. , 2014 ). An L-band SAR image in VH polarization was calibrated to obtain the gamma naught backscattering coefficient: g ¼ 10 Â log 10 ðDNÞ 2 þ CF (1) where g is the gamma naught in dB units, DN is the digital number and CF is the calibration factor (À83. 0 dB). The original 25-m resolution map was resampled to 50 m (using the mean as the resampling method). Height GLCM (Gray-Level Co-Occurrence Matrix) texture indices were calculated using a 3 Â 3 window on the resampled image: mean (SAR_Mean), variance (SAR_Var), homogeneity (SAR_Hom) contrast (SAR_Cont), dissimilarity (SAR_Diss), entropy (SAR_Ent), second moment (SAR_-SecM), and correlation (SAR_Corr).
Digital elevation model data A Digital Elevation Model (DEM) with a spatial resolution of 30 m was obtained from the Shuttle Radar Topography Mission (SRTM). Four variables were derived from the SRTM DEM: elevation (Elev), slope (q), terrain index (TI), surface roughness (Roug), and height above nearest drainage  (Nobre et al. , 2011) (Hand). The TI map was obtained by calculating the difference between the highest and lowest elevations in a 3 Â 3 moving window. The surface roughness map was obtained by computing the standard deviation of the elevation in a 3 Â 3 moving window. All SRTM-derived variables (q, TI, Roug, and Hand) were resampled to 50 m using the mean as the resampling method.
Average rainfall map The rainfall data were obtained from NASA's tropical rainfall measuring mission (TRMM). TRMM data are recorded daily with a spatial resolution of ~25 km. The average rainfall map was computed by averaging daily TRMM rainfall between 2003 and 2013. The average rainfall map was resampled to 50 m. Rainfall was used in AGB mapping because of the strong relationship between precipitation and biomass  (Silvertown et al. , 1994; Yan et al. , 2015). In this study, the average rainfall variable is referred to as "Prec".
Spaceborne ICESat/GLAS LiDAR data In this study, we used satellite LiDAR data that were acquired between 2003 and 2009 by the GLAS sensor onboard the ICESat Satellite. GLAS is a laser sensor operating in the near-infrared wavelength (1064 nm). It illuminates nearly circular footprints with a diameter of approximately 70 m. Spacing between two consecutive footprints in the alongtrack direction is approximately 172 m. The horizontal geolocation accuracy of the GLAS footprints is 3. 7 m (on average)  (Carabajal and Harding, 2006; Huang et al. , 2011). In this study, only the Level-1A altimetry data (GLA01) and Level-2A (GLA14) products available from GLAS were considered. The GLA01 data include transmitted and received waveforms from the GLAS sensor. The GLA14 data are derived from the GLA01 product and include several useful data for each footprint, such as the cloud flag index, saturation waveform index, land surface elevation from SRTM, centroid elevation derived from the waveform, and background noise.
Finally, the mean values of the RS-derived AGB map and the mean values of reference AGB maps were compared through the Root Mean Square Error (RMSE), bias, and Pearson correlation coefficient (R 2 ). The RS-derived AGB map refers to the year 2010 since the used SAR and optical RS variables were acquired in 2010. However, the reference AGB maps refer to the years 2007 (Mabounie), 2011 (Mondah), and 2015 (Lop e and Rabi). Four forest/non-forest maps were considered in the validation of the RS-derived AGB map. These maps correspond to ALOS/PALSAR forest/non-forest products for the years of 2007, 2010 (the map for 2011 is not available) and 2015, as well as to the Sentinel-2 land cover product for 2016 (only this year is available). For the years 2015e2016, it was difficult to choose between the ALOS-derived map and the one based on Sentinel-2 data since the analysis of these two products by photointerpretation showed some artifacts.
ICESat/GLAS surrogate AGB data A RF model that relates the reference AGB from Gabon and French Guiana to GLAS-derived metrics was first built to relate the reference AGB to the GLAS metrics (Wext, LE, TE, H10 through H90 with a 10% step) and DEM variables (slope, TI, and Roug). The database used to build the RF model comprises 68 samples from Gabon and 293 samples from French Guiana. Then, the significant predictive variables were determined based on the %IncMSE. Finally, the RF model was readjusted using only the significant GLAS metrics and DEM variables and applied to obtain the surrogate AGB data, which are AGB values for the 33,275 GLAS footprints throughout forested areas in Gabon.
ICESat/GLAS surrogate AGB data Three hundred and six GLAS footprints located in French Guiana and 55 footprints located in Gabon as well as reference AGB values were used to build an RF model that relates AGB values to GLAS-derive metrics and DEM variables (q, TI, Roug). The results show that Wext, LE, TE, H10, q, and TI are the most significant predictive variables for AGB estimates. From these significant variables, the RF allow AGB estimates with good accuracy (RMSE ¼ 25. 9 t/ha, bias ¼ À0. 1 t/ha and R 2 ¼ 0. 96) (Fig. 7 ). Moreover, the    accuracy on AGB estimates was computed separately for AGB reference data from Gabon and French Guiana. The RMSE is 30. 9 t/ha (bias ¼ 19. 5 t/ha) over Gabon and 24. 9 t/ ha (bias ¼ À3. 6 t/ha) over French Guiana. The calibrated AGB was applied to estimate the AGB from the 33,275 GLAS footprints of forested areas in Gabon.
Conclusions The goal of this study was to map the AGB in Gabon. Four reference AGB maps and RS variables were used. To map the AGB, a RF model that relates the reference AGB map to significant RS-derived variables was first calibrated and then applied. In addition, the possible use of GLAS data through a regression kriging interpolation to improve the RS-derived AGB was investigated. The results showed that the overall RMSE of the RS-derived AGB map was 63. 3 t/ha (bias ¼ 19. 1, R 2 ¼ 0. 53). Moreover, the results show that the RS-derived AGB map highly overestimates the reference AGB for a reference AGB lower than 100 t/ha (bias ¼ 89. 8 t/ ha). This overestimation is potentially related to the soil's contribution to the total backscattered radar signal in Lband. Indeed, the penetration of the SAR signal in the Lband is important, and the emitted signal reaches the ground.
Acknowledgement This research was supported by  IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture)  and the  French Space Study Center (CNES ,  DAR 2018 TOSCA ). The authors wish to thank the  ALOS Research and Application Project of EORC, JAXA  for kindly providing the ALOS images. Also, we thank the  National Center for Scientific and Technical Research (CEN-AREST) ,  Gabon Parks Agency (ANPN)  and the  Gabon Space Agency (AGEOS)  for authorizing and facilitating fieldwork in the country.
Acknowledgments This work has been partially funded by the  Spanish State Research Agency (AEI)  and the  European Regional Development Fund (ERFD)  through the projects DEOCS (ref. MINECO  DPI2016-76493 ) and SCAV (ref. MINECO  DPI2017-88403-R ). This work has also been partially funded by  AGAUR of Generalitat de Catalunya  through the  Advanced Control Systems (SAC) group grant  ( 2017 SGR 482 ). The authors want to thank  VNF  for the support and information provided regarding the inland waterways management.
Sediment recycling in an alluvial fan was recently shown to control the downstream gravel grain size variations in alluvial fans found in an arid Andean piedmont in Argentina  51. Our data confirm that sediment storage and recycling occur even in this relatively short and confined river. Nevertheless, the proportion of recycled gravel in the gravel flux is difficult to estimate. In a first attempt, we can define the proportion of recycled gravel as the proportion of gravel with residence times longer than the residence time predicted in the simple case of steady gravel flux  8. By multiplying the Aroma catchment area by a denudation rate of 10 m/Ma, which is characteristic for this area  52, 53  , we estimate a sediment flux of 10 4 m 3 a -1. The volume of the transported sediment in the river is estimated by multiplying the river length of 56 km by the mean valley width of ~100 m and by a conservative sediment mixing depth of ~10 m.
Acknowledgements ASTER AMS national facility (CEREGE, Aix-en-Provence) is supported by the  INSU/CNRS , the  ANR  through the  "Projets thématiques d' excellence" program for the "Equipements d' excellence" ASTER-CEREGE action ,  IRD. K Keddadouche  and  G. Aumaître  are thanked for their valuable help in AMS measurements. Discussions with P. Maffre were beneficial. This study was funded by  IRD (LMI COPEDIM)  and  INSU/CNRS  ( SESPEED  project). We thank two reviewers and three reviewers on a previous version for their help to improve our manuscript.
To overcome this issue, we only analysed particles on the edge of the spots directly below the micro-orifices of the nozzle plate with CCSEM-EDX. Since the edge was still directly under the nozzle, we assumed that the composition at the edge of the spot was the same as the composition of the whole spot, although we are unable to confirm this assumption. Due to time constraints, only three samples (two with a high [INP(T )] and one with a low [INP(T )]) were analysed by CCSEM-EDX. The method of using CCSEM-EDX to study atmospheric particles is described by  Laskin et al. (2006). Particles with sizes between 0. 15 and 5 µm (area equivalent diameters) were analysed. First, the atomic percentages of each particle were quantitatively determined from EDX spectra. The EDX data for an individual particle represent an average signal from a raster scan.
Specifically, surface coverage data from the NSIDC were downloaded in GEOtiff format and converted to vector shape files. The fraction of each FLEXPART grid cell that was over each surface type category (e. g. bare land, open water) was then calculated using these vector shape files. The residence time in a grid cell was then multiplied by the fraction of the cell in each surface type category, and then the results were summed over all grid cells to determine the relative time spent over each surface type. Correlations between the total residence time over each surface type and the concentration of INPs for each sample at -15, -20, and -25 • C were then investigated (Table  1  and Fig. S7 ). This correlation analysis showed statistically significant (p < 0. 05) positive correlations between the total residence time over bare land in the footprint layer, and both [INP(T )] values at -15 • C (R = 0.
Acknowledgements. We would like to thank the scientists, officers, and crew of the  CCGS Amundsen  for support during the expedition in 2014. We acknowledge the use of data products or imagery from the  Land, Atmosphere Near real-time Capability  for EOS (LANCE) system operated by the  NASA/GSFC/Earth Science Data and Information System (ESDIS)  with funding provided by  NASA/HQ. The CCSEM-EDX analysis was performed at the  Environmental Molecular Sciences Laboratory at Pacific Northwest National Laboratory  (PNNL; Ringgold IDs 47937 and  48781 ), a  US DOE Office of Science user  facility sponsored by the  Office of Biological and Environmental Research. PNNL is operated for DOE by  Battelle Memorial Institute  under contract  DEAC06-76RL0 1830. We would also like to thank the  Natural Sciences and Engineering Research Council of Canada and Fisheries and Oceans Canada  for funding.
The measurement plane was set normal to the free flow, behind the DelFly tail and an advective approach (Taylors hypothesis) was applied to reconstruct an estimate of the three dimensional wake configuration. A similar approach has been used in a variety of animal studies  [28, 52, 53, 54]. The DelFly was controlled by the on-board autopilot, which was steering it towards a desired position set-point based on feedback from the external motion tracking system. An operator was monitoring on-line the position errors and triggered the measurement at a convenient moment. He would also repeat the measurement in case the errors were too large. Additional IR LEDs, fixed with respect to the ground and detected by the tracking system, were turned on together with the trigger signal to the PIV system, which served as a time stamp for time synchronisation of the tracking and PIV data sets.
Images of tracer particles were captured with two high-speed Photron FastCam CMOS cameras which allows to achieve a maximum resolution of 1024 × 1024 pixels at a data rate of up to 5. 4 kHz. Each camera was equipped with a Nikon 60 mm focal objective with numerical aperture f/4 and mounted with Scheimpflug adaptors. The cameras were placed with an angle of 40 • with respect to each other. A schematic overview of the PIV measurement setup can be seen in figure  3. A field of view of 170 mm × 170 mm was captured with a magnification factor of approximately 0. 12 at a digital resolution of 6 pixels/mm. Single-frame images were recorded at a rate of 5 kHz for approximately a second, yielding a data ensemble size of about 5000 images. The associated time interval of 0. 2 ms between individual frames corresponds to an out-of-plane displacement of 0.
4 mm, based on the free stream velocity. This is sufficiently low with respect to the laser sheet thickness to allow for an accurate correlation of subsequent particle images. In order to increase the signal-to-noise ratio in the images, two laser cavities were shot in each single camera frame with 1 µs time delay in between, ensuring frozen particle images. The commercial software Davis 8. 0 (LaVision) was used in data acquisition, image pre-processing, stereoscopic correlation of the images, and further vector post-processing. The pre-processed single-frame images were interrogated using windows of final size of 64 × 64 pixels with two refinement steps and an overlap factor of 75% resulting in approximately 4800 vectors with a spacing of 2. 9 mm in each direction. A spatio-temporal reconstruction was performed for the initial interpretation of the wake structure.
Steady state The results of steady flight with a fixed position set-point, performed at wind tunnel set-points V W = 0. 8 m/s, 1. 2 m/s, 2. 0 m/s and 2. 4 m/s, are in figure  9. Each panel shows the difference from the set-point in all three wind tunnel axes. The highlighted parts (thicker line, red colour) show the segments, where a successful PIV measurement could be performed according to our estimations, i. e. where the root mean square error (rms) remains below 25 mm in all three axes for the next 2 seconds. The rms errors over the whole measurement are summarised, together with mean body attitude angles, mean marker tracking errors and their respective standard deviations, in table  3. All the data was measured by the motion tracking system. It can be immediately noted that the performance at low speeds is much better than at high speeds.
Climate data Local climate has been daily monitored in situ since 1976 and 1960 for temperature and precipitation/mean relative humidity, respectively. In order to obtain a complete climatic series (from 1959 to 2016), we used the quantile mapping and anomaly method in the R package "meteoland" (De  Caceres et al. 2018) , considering the 8-km-resolution-SAFRAN reanalysis  (Vidal et al. 2010)  as reference. From the corrected climate series, we derived the daily climatic input variables for CASTANEA, which are the minimum, mean and maximum temperatures (in °C), the precipitation (mm), the wind speed (m. s -1 ), the mean relative humidity (%) and the global radiation (MJ. m -²).
We expressed the results in terms of odds ratios, indicating the degree of dependency between variables. For instance, the odds ratio for mortality as a function of budburst characteristics (early vs normal) is: OddsRatio Early. vs. Normal = Od Early Od Normal (Equation  9 ), With Odds Early = P mortality (Early) 1-P mortality  (Early)  and Odds Normal = P mortality (Normal) 1-P mortality  (Normall). We computed odds ratios with "questionr" the R package  (Barnier, Briatte, and Larmarange 2018). The interactions were visualized with the package "jtools"  (Long 2018).