dataesr/scientific-paragraphs-categorization huggingface dataset:
220 °C, reaching its maximum around 270 °C and then coexisting with a small fraction of Pt NPs (red spectrum) for 1 hour at 300 °C. The position of the white line in this peak is shifted towards higher energies. It is important to note that although small amounts of this component are present in the series of spectra collected in H 2 , only the dataset collected in He makes this component statistically relevant. Finally, the fourth spectrum (green) is present under all activation conditions up to 250 °C. This spectrum is similar to the first one (dark grey) but has a slightly lower intensity of the white line and the spectral features are shifted towards lower energies (e. g. peak C at ~ 11610 eV is shifted to the left by 3 eV), which is an indication of the increasing interatomic distances.
Acknowledgments A. L. B. and A. A. S. acknowledge  Russian Science Foundation  project No. 18-73-00189  for funding the research. A. L. , U. O. and K. P. L. acknowledge the  Research Council of Norway  project  250795. We are indebted to  Elisa Borfecchia  and  Andrea Martini  for fruitful discussion. We thank  Suresh Gatla ,  Olivier Mathon  and  Sakura Pascarelli  for their assistance during experiment at BM23. We also thank  Vladimir Dmitriev ,  Herman Emerich ,  Wouter van Beek  and  Michela Brunelli  for the friendly and competent support during the experiments performed at  BM01B  and  BM31  beamlines of the  ESRF.
Inventory data Our study includes data from 15 long-term (8-30 year) experimental forest sites (845 ha total) in the Amazon Basin and on the Guiana Shield (figure  S1 (a) is available online at stacks. iop. org/ERL/14/064014/mmedia) that are part of the TmFO network  [11]. Plots were subjected to conventional logging (8% of plots), reduced-impact logging techniques (e. g. skid-trail planning and directional felling; 33%), post-logging liberation thinning (37%), and control plots with no logging (22%). All sites are located in terra firme forests with mean annual precipitation 1000 mm, experienced different logging intensities, and have at least one prelogging census and two post-logging censuses. In each plot, all stems with diameter at breast height (DBH) 50 cm were measured; 82% of trees were identified to species and 15% to genus.
For sites with plots 1 ha, data from those with the same treatment were aggregated to mitigate the small plot effect on the variation in density of large trees. Additionally, single measurement plot data from the RadamBrasil project  [13]  were made available by the Brazilian Institute of Geography and Statistics  [14]. We used 2646 1 ha forest inventory plots from across the Brazilian Amazon (figure S1(b)) in which all trees 33 cm DBH were measured and identified to species between 1973 and 1982.
Spatial data Environmental data for both study plots and Amazonscale extrapolation were extracted from WorldClim 2. 0  [15]  (precipitation, seasonality of precipitation and solar radiation) and SoilGrids  [16]  (bulk density, CEC, soil depth, proportion of clay, of sand and of coarse fragment) at a 1 km resolution. When extrapolating to make regional predictions, spatial data was aggregated to a 1°grid by averaging values of all 1 km pixels inside areas available for logging in each 1°cell. Annual stem mortality rates (as a proportion of live stems), estimated with the metadata from Johnson and colleagues  [17] , were extracted from the Forest-Plots database  [18]  and interpolated with the R package gstat  [19]  on a 1°resolution grid. The climax volume, and the gross volume productivity at climax were estimated with the individual-tree-based gap model FORMIND  [20].
Climax volume was calculated as the volume of all trees 50 cmDBH (per ha) in an old-growth forest; climax gross volume productivity (GVP in figure  1 ) was calculated as the gross volume gain from photosynthesis (before accounting for respiration losses) of trees 50 cm DBH in an oldgrowth forest. Raster maps of climax volume and climax gross volume productivity were created at 1 km 2 resolution, and values were then aggregated to a 1°r esolution grid. The map of areas available for logging (figure  S4 ) was constructed as the intersection of 3 maps: a buffer of 25 km around all roads and motorable tracks from the OpenStreetMap database  [21] ; the map of areas outside of protected areas from the World Database on Protected Areas  [22]  (except the category VI of the IUCN classification, i.
Conclusion Selective logging in Amazonian forests cannot provide enough timber to meet even the current regional demand over the long term. The 'light' scenarios (low intensity and long cutting cycles) do not provide enough timber and the 'heavy' scenarios are not sustainable, insofar as they do not allow volume recovery during a cutting cycle (figure  4 ); moreover, future deforestation, forest degradation and climate change will likely worsen the picture. These results call for a re-evaluation of the strategy for future timber provision in Amazonia. We are in a period of transition that requires important forest policy decisions that promote diversification of timber sources and a substantial shift in the objectives of Amazonian production forest management. LABEX-0025, and the REsilience of Managed Amazonian FORests project funded by LabEx Agropolis: ANR-10-LABX-0001), the Sao Paulo Research Foundation (FAPESP: 2013/16262-4 and 2013/50718-5), and Embrapa, and carried out in the framework of the Tropical managed Forests Observatory (TmFO), supported by the Sentinel Landscape program of CGIAR (Consultative Group on International Agricultural Research)-Forest Tree and Agroforestry Research Program.
Missing data in these covariates were imputed with simple imputation, with the exception of collateral grade (≥10% missing data), which was imputed with single imputation with regression based on relevant covariates, trial, and mRS. Unadjusted and adjusted common odds ratios and adjusted betas are reported with 95% CIs, and all P are 2-sided. Statistical analyses were performed with SAS software, version 9. 4, and R, version 3. 3.
2010) at 11 different locations across the globe are used to assess potential improvements, especially in the stratosphere. The observations used for 13 C: 12 C isotopic ratio comparison are balloon measurements presented in  Röckmann et al. (2011). Section 2 presents the model and data used to run the simulations as well as observations. In Section 3, we analyse first the impact of Cl on the total methane and then on methane isotopic ratio. Section 4 presents a discussion and conclusions.
Total CH 4 surface fluxes The surface fluxes prescribed to simulate total methane mixing ratios are the ones suggested by the Global Carbon Project (GCP) as a priori emissions for inversions performed for the Global Methane Budget  (Saunois et al. , 2019). Anthropogenic (including biofuels) and fire emissions for the 2000-2017 period are taken from bottom-up estimates provided by the EDGARv432 (http://edgar. jrc. ec. europa. eu/overview. php?v=432SECURE=123, Janssens-Maenhout et al. , 2017) and the GFED4s databases  (van der Werf et al. , 2017) , respectively. Statistics from British Petroleum (BP) and the Food and Agriculture Organization of the United Nations (FAO) have been used to extend the EDGARv432 database, ending 2012, until 2017. The natural sources emissions are based on averaged literature values :  Poulter et al.
, 2010)  and 35 by the French AirCore Program  (Membrive et al. , 2017). Those vertical profiles have all been collected using the AirCore technique  (Karion et al. , 2010). This technique retrieves air samples going from the surface to approximately 30km. The locations and the number of profiles associated are shown in Figure  2. This dataset has also been used in this study to analyze the seasonal vertical trend of CH 4 in both the troposphere and stratosphere and the model ability to reproduce the observations depending on the season. A summary of the information about the provider, the location and the number of profiles retrieved at this location is given in Table  3. The CRDS analyzer precision of the measurements of the AirCore sample for both NOAA and French AirCore Program is less than 2 ppb for CH 4 which is generally much smaller than the model-observation mismatch.
We compute the difference between simulated and observed profiles after towards smaller values (reduction ratio smaller than 100%) when including the Cl sink for all seasons in both the troposphere and stratosphere (Fig. 5 ). This illustrates the improvement of the model when including Cl. We find that the implementation of the Cl sink reduces the difference between simulated and observed vertical profiles on average by 26. 7 ± 9. 73 % (1σ for intra-profile variability) in the stratosphere over the entire dataset and by 38. 3 ± 13. 8 % in the troposphere. This ratio represents a CH 4 reduction of about 24 ppb in the stratosphere and 8. 1 ppb in the troposphere. However, even including the Cl sink, the simulations shows significant remaining discrepancies in the troposphere compared to AirCore observations (∼20 ppb, 1,1% of the tropospheric mixing ratio) and in the stratosphere (∼85 ppb, 5.
5% of the stratospheric mass-weighted mean mixing ratio). The reason of this systematic error in the troposphere might be due to the overestimation of the emission scenario from GCP which reflects the state of the art of emission scenarios but is not optimized against atmospheric observations and not in Figure  5  shows the seasonal distribution of the differences (and reduction ratio) between the simulated and observed by AirCore methane mixing ratio for the tropospheric and stratospheric layers with and without the Cl sink. The AirCore datasets provide multiple profiles for each season. Seasons are referred hereafter as North Hemispheric seasons, i. e winter meaning boreal winter. Note that an AirCore profile retrieved above South Hemisphere locations is included in the list associated to its true season.
For instance, an AirCore dataset retrieved during January in the South Hemisphere is added to the summer list. The displayed value in each frame is the mean value of these differences over the considered region, namely troposphere or stratosphere. The reduction ratio (third line and sixth line) ranges from 32 % (spring) to 43% (autumn) in the troposphere and from 23% (spring) to 33% (winter) in the stratosphere. Therefore, we have larger reduction ratios in the troposphere than in the stratosphere, regardless of the season. The reduction is in a range of 6. 69 (autumn) -10. 50 (spring) ppb in the troposphere and 19. 26 (winter) -28. 04 (summer) ppb in the stratosphere. The reduction is thus much lower (more than 10 ppb) in the troposphere than in the stratosphere. Nevertheless, the reduction in each region of the atmosphere is dependent on the season.
use the LMDz-INCA model to simulate beforehand the OH, O( 1 D) and Cl three-dimensional and time dependant concentrations to be prescribed in our methane simulations. LMDz-INCA couples the INCA [INteraction with Chemistry and Aerosols] (Folberth et al. , 2006; Hauglustaine et al. , 2004 ) and the LMDz GCM described in Section 2. 1. Seventeen ozonedepleting substances made up of five CFCs (CFC-12, CFC-11, CFC-113), three HCFCs (HCFC-22, HCFC-141b, HCFC-142b), two halons (Halon-1211, Halon-1301), CH 3 CCl 3 , CCl 4 , CH 3 Cl, methylene chloride (CH 2 Cl 2 ), chloroform (CHCl 3 ), methyl bromide (CH 3 Br) and HFC-134a and the associated photochemical reactions are included in the INCA chemical scheme to produce Cl(Terrenoire et al. , in preparation, 2019). In the LMDz-INCA simulations, the surface concentrations of these longlived Cl source species are prescribed according to the historical datasets prepared byMeinshausen et al.
Acknowledgements. This work was supported by the  CEA  ( Commissariat à l'Energie Atomique et aux Energies Alternatives ). We are grateful to  Thomas Röckmann  for sharing his data and for insightful discussion. The study extensively relies on the meteorological data provided by the ECMWF. Calculations were performed using the computing resources of LSCE, maintained by  François Marabelle  and the LSCE IT team. The authors wish to thank the measurement teams from the  Global Greenhouse Gas Reference Network (NOAA)  for their work.
Data availability. The data for δ 13 C-CH 4 observations were downloaded from the World Data Centre for Greenhouse Gases (WDCGG) at https://gaw. kishou. go. jp. Datasets for the input emissions were provided by the Global Carbon Project (GCP) team. The AirCore vertical profiles from the NOAA-ESRL Aircraft Program (v20181101) were provided by Colm Sweeney and Bianca Baier. The Cl fields, the modelling output files and the AirCore vertical profiles from the French AirCore Program are available upon request from the corresponding author.
Less than two years after the launch of the first Landsat satellite in 1972 (then Earth Resources Technology Satellite 1 or ERTS-1), a conference paper was presented using satellite data for BA mapping  (Hitchcock and Hoffer, 1974). Since then, the use of satellite imagery for BA detection and mapping has been addressed in many peer-reviewed journals, book chapters, and conference proceedings. This review paper aims to evaluate the historical developments of satellite-based studies on BA estimation, the different sensors and methods that have been used, and the strengths and limitations of current available BA products, with particular emphasis on global datasets. We focus on BA mapping, assuming a binary detection (burned/ unburned). Analysis of fire effects or regeneration after fire has been covered elsewhere  (Chu and Guo, 2014; Storey et al.
, 2006). By the early 2000s, the integration of satellite observations allowed for a better representation of the spatial and temporal variability of fire emissions  (Duncan et al. , 2003; Ito and Penner, 2004 ) that relied on the first available global BA maps computed from satellite information of post-fire reflectance: the GBA2000 product  (Grégoire et al. , 2003)  and GLOBSCAR  (Simon et al. , 2004). The first version of the Global Fire Emission Database (GFED) then provided 1°× 1°gridded monthly fire emissions from 1997 to 2002  (van der Werf et al. , 2003 (van der Werf et al. , , 2004)). GFED uses gridded 0. 5°or 0. 25°BA data in a biogeochemical model where available biomass to burn in vegetation and soil is determined by coarse scale land cover types, soil types and their corresponding water holding capacity and climate (CASA).
However, bias may be introduced when emissions are simulated at coarse resolution as fires may be highly selective so that some places might have high fire frequency while other places may never burn  (Barros and Pereira, 2014, Scholz et al. 2014). Therefore, regional studies using fine resolution datasets are preferred  (Veraverbeke et al. , 2015; Yospin et al. , 2015). Mid-resolution global remote sensing datasets recently provided additional information on the distribution of fire within coarse resolution grid cells to estimate reburning rates and vegetation types or forest age cohorts affected by fires. In GFED version 2, fire emissions estimates relied on BA time series that were established by calibrating HS to the first and, at that time, only regionally available direct BA maps from the MODIS sensor  (Giglio et al.
, 2013). These applications require low spatial and temporal resolution but long-term emission inventories (much longer than available remotely sensed BA time series) with high temporal stability in accuracy so that long-term effects of fires on tree age cohorts and their impact on carbon budget are taken into account  (Yue et al. , 2014). Such inventories rely on national statistics, historical regional remote sensing BA estimates and storylines  (Mouillot and Field, 2005; Mouillot et al. , 2006; Mieville et al. , 2010) , or the global charcoal database and fire models embedded in dynamic vegetation models (DGVMs)  (van Marle et al. , 2017). Beside direct aerosol emission from biomass burning, the climate system is also linked to the terrestrial global carbon budget affected by fires through the combustion of live and dead biomass and soil litter and the subsequent post-fire changes in vegetation and surface fluxes.
, 2016; Nogueira et al. , 2017). They can be generated by aggregating neighboring pixels with near-similar burn-dates  (Archibald et al. , 2009; Hantson et al. , 2015). Fire patch size, complexity, and unburned islands also influence tree species colonization rates from seed dispersal  (Meddens et al. , 2018) , affecting subsequent carbon sequestration dynamics  (Duncan and Duncan, 2000; Caughlin et al. , 2016). Laurent et al. (2018)  estimated patch complexity, elongation and rate of spread from a global 250-m BA dataset, to estimate proxies on fire spreading processes to improve fire modules within DGVMs. Beside these main topics for which global BA was initially developed, BA information is also essential for legal purposes at local to regional scales as for proper cadastral recording of fire effects, regarding land use and insurance costs or illegal burning  (Mouillot et al.
The coherence provides a means to estimate the correlation between the backscattered signal from a given target seen under two slightly different acquisition geometries and offers additional information on scene properties. Lastly, the availability of fully polarized (VV, HH, VH, and HV polarizations) datasets allows for a complete description of the scattering process with polarimetric target decomposition techniques being designed to enhance or suppress contributions from specific scattering mechanisms thus allowing for improved retrieval of the biophysical characteristics of interest. The use of SAR-based techniques provides distinct advantages over other sensor types including sensitivity to vegetation structure and frequent cloud-free acquisitions. Different wavelengths such as X-, C-, and L-bands (i.
Principal component analysis (PCA) has been used since early 1980s for BA and change detection analysis  (Richards, 1984). Several approaches used PCA in BA mapping from: (i) an 8-dimensional multi-temporal image dataset consisting of two Landsat MSS scenes  (Richards, 1984) , (ii) a standardized PCA on a 12-dimensional multi-temporal image dataset consisting of two Landsat TM scenes, and a subset of spectral channels consisting of pairs of two spectral channels with low to medium correlation  (Pereira 1992 ), (iii) multi-temporal ERS-2 SAR images acquired before and after the fire event  (Siegert and Ruecker, 2000) , (iv) a non-standardized PCA on a multi-temporal dataset comprising TM bands 3, 4, and 5 from both dates  (García-Haro et al. , 2001) , (v) a dataset consisting of a standardized NDVI, surface temperature and albedo from a NOAA AVHRR time series dataset  (Nielsen et al.
, 2002) , (vi) a standardized PCA along with a simple, non-parametric, supervised classification (parallelepiped) on a Landsat time series dataset consisting of 22 annual images of Landsat MSS, TM and Enhanced Thematic Mapper Plus (ETM+) from 1972 to 2002 (Hudak and Brockett 2004), and (vii) a forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burned areas  (Koutsias et al. , 2009). Finally, the third group of studies refers to the classification techniques, on whether supervised and unsupervised methods were used. This depends on the previous knowledge of fire effects in the target region. The maximum likelihood classification and k-means clustering algorithms were employed in several studies either to directly map BA or used to evaluate other classification techniques  (Henry 2008; Pereira and Setzer, 1993).
, 2015; Verbesselt et al. , 2010). Other approaches decomposed dense time series to separate seasonality and long-term trends and detect change as departure from those trends using Landsat data  (Brooks et al. , 2014; Zhu and Woodcock, 2014). Finally, others have detected change using decision trees with predictors characterizing the entire Landsat time series  (Hansen et al. , 2014). All of these methods detect abstract land change and require additional attribution to characterize the specific type of change, such as BA  (Schroeder et al. , 2017; Zhao et al. , 2015). Change-detection algorithms specific to mapping BA across the full Landsat archive have also emerged. Using all available Landsat data for Queensland, Australia,  Goodwin and Collett (2014)  combined decision rules with a region-growing algorithm to identify areas of change and then classified which of those areas of change were caused by fire.
In forested parts of the western US,  Boschetti et al. (2015)  identified areas of spectral change using composites of Landsat 7 ETM+ data from 2002 and then used MODIS active fire data to separate BA from other types of change. These two studies were the first to demonstrate that large territories can be routinely mapped from Landsat data with semi-automated approaches and paved the way for the development of the U. S. Geological Survey's Landsat Burned Area Essential Climate Variable (BAECV)  (Hawbaker et al. , 2017). The BAECV algorithm was developed to consistently map burned areas ≥4 ha across the conterminous U. S. regardless of ecosystem type using all available Landsat data by combining a gradient boosted classifier with thresholding and region growing. Landsat-based studies also set forth the development of approaches to map BA using data from the Sentinel-2A and 2B missions, which provide free accessible images, in 13 spectral channels (from 10 to 60 m of spatial resolution) and with a combined 5-day coverage period.
Such studies used empirically derived thresholds applied to temporal differences of pre-and post-fire coherence estimates  (Liew et al. , 1999). More recent studies focused on radar polarimetric properties  (Goodenough et al. , 2011)  and integrating radar and optical datasets within common detection algorithms  (Stroppiana et al. , 2015)  or through integration of the radar and optically detected burned areas  (Verhegghen et al. , 2016). Such studies demonstrated that fires result in ambiguous effects depending on the radar wavelength, polarization, and meteorological conditions at image acquisition  (Lohberger et al. , 2018; Ruecker and Siegert, 2000 )  (Imperatore et al. , 2017; Polychronaki et al. , 2013; Tanase et al. , 2010b ) (Bourgeau-Chavez et al. , 2002; Huang and Siegert, 2006; Kasischke et al.
, 1994)   (Gimeno and San-Miguel-Ayanz, 2004)   (Menges et al. , 2004). SAR data were also used to estimate fire impacts from the backscatter coefficient  (Kurum, 2015) , the interferometric coherence  (Tanase et al. , 2010a)  or through polarimetric decomposition techniques  (Tanase et al. , 2014). Most studies used post-fire images  (Bourgeau-Chavez et al. , 1994; Tanase et al. , 2010a; Tanase et al. , 2010b)  or change detection frameworks based on pre-and post-fire datasets  (Kurum, 2015; Tanase et al. , 2015b)  while few authors focused on the synergy between optical and radar sensors  (Tanase et al. , 2015a). The most accurate results were obtained using the cross-polarized (HV) backscatter and longer wavelengths such as the L-band with the retrieval accuracy being negatively influenced in areas of steep topography or with high soil moisture  (Kalogirou et al.
, 2014; Kasischke et al. , 2007; Tanase et al. , 2010b). The influence of topography was removed through change detection approaches while the use of datasets acquired under dry environmental conditions or multi-temporal averages were suggested to reduce the effect of varying soil and vegetation moisture content  (Tanase et al. , 2015b; Tanase et al. , 2010a). In addition, the dependency of in situ data for modeling was eliminated by using polarimetric decomposition techniques  (Tanase et al. , 2014). However, the scarcity of full polarimetric acquisitions has precluded the use of polarimetric analysis over large areas.