dataesr/scientific-paragraphs-categorization huggingface dataset:
5 %; 241 Pu, 0. 3 %; 242 Pu, 0. 07 %; 243 Pu, 0. 07 %), potassium ( 40 K, 1. 5 %), radium ( 226 Ra, 0. 96 %; 228 Ra, 0. 14 %), strontium ( 90 Sr, 2. 8 %), technetium ( 99 Tc, 0. 07 %), thorium ( 228 Th, 0. 3 %; 232 Th, 0. 3 %; 234 Th, 0. 37 %) and uranium ( 236 U, 0. 07 %; 238 U, 0. 3 %). The 14 C was analyzed in 4. 5 % of the sediment cores. This isotope was exclusively analyzed by accelerator mass spectrometry (AMS) in the articles included in the database for providing long-term chronology. https://doi. org/10. 5194/essd-13-4951-2021 Earth Syst. Sci. Data, 13, 4951-4966, 2021.
General patterns of 137 Cs fallout According to  UNSCEAR (2000) , 77 % of the total worldwide fallout of 137 Cs is estimated to have occurred in the Northern Hemisphere. Previous studies have identified two main sources of 137 Cs in Europe, including the fallout associated with the thermonuclear weapon tests and that emitted by the Chernobyl accident  (Meusburger et al. , 2020). In the current database, 137 Cs deposition associated with aboveground nuclear tests was largely detected across the European continent with a peak systematically assigned to 1963 (Fig. 4 ). This peak was described in the literature as the most reliable time marker worldwide  (He and Walling, 1997). In addition, no technical problem was reported to detect the occurrence of fallout associated with the Chernobyl accident.
Limitations of the currently published 137 Cs research This worldwide database also underlines the unexpected detection of 137 Cs sources in certain regions of the world. In China, a large number of studies claim to have detected Chernobyl fallout, whereas more regional sources of 137 Cs attributed to the Chinese and Russian nuclear tests conducted in Lop Nor and Semipalatinsk, respectively, are not considered a potential origin  (Huang et al. , 2019; Zhao et al. , 2020). The potential impact of these tests should be investigated more thoroughly. Conversely, the attribution of a peak to the Chernobyl fallout should be avoided without clear isotopic evidence (e. g. , plutonium isotopes), as the associated fallout was not observed in a vast region that extends from the Ural Mountains/eastern Turkey to China.
, 2005). Furthermore, earlier deposits of 137 Cs on ice and snow were reported to have increased the 137 Cs activity in a sediment sequence of Antarctica after ice melting  (Sanders et al. , 2010). Conversely, certain storm events and the associated flood deposits can lead to the dilution of the 137 Cs signal  (Gu et al. , 2011). In addition to these natural and anthropogenic processes that may lead to an increase or a decrease in the 137 Cs activity along sedimentary sequences, a large number of studies included in the current database reported the occurrence of post-depositional processes. Among them, bioturbation and post-depositional mobility/diffusion may disturb the temporal profile of radionuclides with depth along the core and dilute their concentrations  (Benninger et al. , 1998; Larsen et al.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-13-4951-2021-supplement. Author contributions. AF and OE contributed to the dataset compilation. AF, PAC, PS and OE participated in the results discussion and drafted the main manuscript. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The authors are very grateful to the scientific community using fallout radionuclides and especially to those colleagues who published their raw data for dating sediment cores without whom the current overview would not have been possible.
Indicators and metrics based on multispectral satellite imagery (visible and infrared) are the most frequently used, such as the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized burned ratio (NBR)  (Pérez-Cabello et al. , 2021). Despite their fast saturation over dense forests, they still provide a good proxy for green vegetation regrowth. Microwave data also shown a good potential to monitor post-fire biomass recovery. Mermoz and Le Toan (2016)  used L-band ALOS PALSAR to assess forest regrowth in South-East Asia. Fernandez-Carrillo et al. (2019)  used active L-band SAR data to estimate the tree survival in eucalyptus forests of Western Australia. Zhang et al. (2021)  analysed forest canopy dynamics in the southern Amazon during the 2019 fire season using passive microwave vegetation optical depth at C-band (C-VOD).
Precipitation Precipitation (P) data come from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). The precipitation estimate uses the PERSIANN algorithm on GridSat-B1 infrared satellite data, and training of the artificial neural network on the National Centers for Environmental Prediction (NCEP) hourly precipitation data  (Ashouri et al. , 2015). The dataset is distributed by National Oceanic and Atmospheric Administration (NOAA) at a daily time scale, and at 0. 25 deg resolution in the latitude band 60°S -60°N.
Soil Moisture The soil moisture (SM) dataset comes from SMOS satellite, launched by the European Space Agency (ESA) in 2009  (Kerr et al. , 2001). It performs passive measurements of the thermal emission of the Earth at L-band (1. 4 GHz, 21 cm). L-band VOD and SM are derived from SMOS brightness temperatures using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model  (Wigneron et al. , 2007; Kerr et al. , 2012). L-band SM is the volume of water per volume of soil (m 3 m -3 ) in the top surface soil layer (~5 cm). The footprint size is ~43 km in average  (Kerr et al. , 2010). We considered the ESA level 2 SM dataset in version 7. 2 (L2 v720) resampled to the global cylindrical Equal-Area Scalable Earth (EASE) Grid version 2. 0  (Brodzik et al. , 2012)  at 625 km² spatial sampling (25 km × 25 km at 30 deg of latitude).
Terrestrial Water Storage Terrestrial water storage (TWS) anomalies from the Gravity Recovery and Climate Experiment (GRACE) satellite were also considered. We used monthly GRACE/GRACE-Follow On (FO) Level-3 product provided through the Gravity Information Service (GravIS) web portal of the German Research Centre for Geosciences (GFZ) at 1 deg latitude-longitude grids  (Boergens et al. , 2019). TWS anomalies represent the water mass anomalies from snow, surface water, soil moisture, and deep groundwater. They are derived from measurements of temporal changes in the Earth's gravity field. Data were lacking for 35 dates of the ten-year dataset. One-time gaps were filled by linear interpolation; consecutive missing months were not considered.
Temperature Temperature (T) data come from the Land Surface Temperature (LST) dataset from MODIS Terra satellite (NASA). Daytime and night time measurements were averaged (MOD11C3 Version 6 product in a Climate Modeling Grid (CMG), LST_Day_CMG and LST_Night_CMG,  Wan et al. , 2015). These datasets are obtained using MODIS thermal infrared bands from 3 to 15 μm, and distributed by NASA Land Processes Distributed Active Archive Center (LP DAAC) at a monthly time scale and at 0. 05 deg resolution.
Vegetation Optical Depth Vegetation optical depth (VOD) is a remotely sensed indicator related to AGB and to vegetation water content (VWC)  (Kerr and Njoku, 1990; Jackson and Schmugge, 1991; Jones et al. , 2011; Rahmoune et al. , 2014; Vittucci et al. , 2016; Rodriguez-Fernandez et al. , 2018; Mialon et al. , 2020; Konings et al. , 2021). The lower frequencies have better capabilities to penetrate deeper within the canopy  (Ulaby et al. , 1981). At L-band, VOD is sensitive to coarse woody elements, such as trunks, stems, and branches. At C-and X-band, VOD is more sensitive to thin stems and leaves  (Guglielmetti et al. , 2007). In this paper, L-VOD comes from SMOS level 2 dataset in version 7. 2 (L2 v720) measured at 1. 4 GHz (λ = 21 cm), resampled to EASE-Grid 2. 0 at 625 km² resolution (25 km × 25 km at 30 deg of latitude).
In the SMOS retrieval algorithm, the vegetation attenuation is taken into account by the τ parameter of the τ -ω model  (Mo et al. , 1982)  which corresponds to the L-VOD. Data from June 2010 to December 2020 were considered, and ascending (6 am) and descending (6 pm) overpasses were averaged. C-and X-VOD from the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission (GCOM) Advanced Microwave Scanning Radiometer (AMSR)-2 dataset were also considered  (Imaoka et al. , 2010). C-and X-VOD are measured at 6. 9 GHz (λ = 4. 3 cm) and 10. 7 GHz (λ = 2. 8 cm) respectively. C2band (7. 3 GHz, λ = 4. 1 cm) was not discussed in this paper as the data were mostly redundant with C1-band (6. 9 GHz). We used the daily L3 V001 VOD products, from July 2012 to December 2020, processed with the Land Parameter Retrieval Model (LPRM) algorithm  (Owe et al.
Year of gross forest cover loss event The year of gross forest cover loss event map (the so-called lossyear product) from  Hansen et al. (2013)  was used to observe the forest loss rate and year within a SMOS pixel, for the three major fires studied (Fig. 2 ). This map represents the first year of detected tree loss during the period 2000-2020, defined as a standreplacement disturbance, or a change from a forest to non-forest state. This dataset is based on Landsat images and is distributed at ~30 m resolution with 10x10 square degree tiles at https://glad. earthengine. app/view/global-forest-change. Each year of the period 2010-2020 was extracted from the forest loss product and averaged into SMOS EASE-Grid 2. 0, so as to obtain a yearly percentage of forest loss.
Land cover A land surface climatology map based on 10 years  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  of the MODIS MCD12Q1 product at 500 m resolution  (Broxton et al. , 2014)  was used to filter the data and to distinguish four different vegetation types (see Sect. 3). This land cover map allows to identify 17 ecosystems based on the IGBP (International Geosphere-Biosphere Programme) class labels.
Four CVs related to the fire risk were considered: precipitation, SM, TWS, and temperature. Wind is another predominant fire likelihood factor  (Albini, 1993) , but was not studied here due to the lack of global reliable Monthly averages of all datasets were computed and resampled to SMOS grid. To do so, they were first oversampled to 1 km resolution and then averaged to SMOS EASE-Grid 2. 0 (~25 km resolution) with GDAL (GDAL/OGR contributors, 2020).
Extension to the global scale Fires were then studied at the global scale to assess the general factors related to fire risk and the response of vegetation to fire according to the ecosystem. Several areas were excluded: i) snow-covered months (20% maximum monthly snow coverage), because snow interferes with most satellite observations; ii) flooded areas, with a maximum threshold of 10% on GIEMS-2 water fraction climatology, because standing water perturbs VOD measurements  (Bousquet et al. , 2021) ; iii) Australia because numerous and severe events took place in 2019/2020, which prevailed over the global dataset and prevented to perform a robust post-fire study. The global scale was divided into five land cover classes: needleleaf forests (IGBP labels 1 and 3), sparse broadleaf forests (IGBP labels 2 and 4, AGB ≤ 150 Mg ha -1 ), dense broadleaf forests (IGBP labels 2 and 4, AGB > 150 Mg ha -1 ), tropical and subtropical savannas (IGBP labels 8 and 9, latitudes between 40°S and 40°N), and grasslands (IGBP label 10).
Only the range July 2012-December 2020 was conserved here for all datasets so as to match with AMSR-2 time-period. Fires with a number larger than 5 in the MODIS dataset were considered, and only if no other fire occurred on the same area (number lower than 2 apart from the main fire event). This was done to observe only the impact of the major fires, without any other disturbance. These thresholds were tested and determined empirically. CVs and VVs anomalies were computed with Eq. (  1 ) and (  2 ), with a climatology over all dates excepted the year of the fire (from -5 to +6 months from the fire date tfire), in order to remove these exceptional values. The anomalies time series were then shifted to collocate in time all fire events. Averaged anomalies for a month m were kept only if at least one quarter of the dataset was available for that month m, in order to be representative of the global dataset.
3. 2) based on the Global Inundation Estimate from MultipleSatellites (GIEMS-2) dataset (Prigent et al. , 2019). It provides long-term monthly estimates of surface water extent, including open watern wetlands, and rice paddies. The methodology combines passive and active microwaves, visible and near-infrared observations (SSM/I, ERS, AVHRR). The water fraction is delivered globally from 1992 to 2015, on an equal area grid of 0. 25 deg × 0. 25 deg at the equator (~28 km × 28 km).
https://doi. org/10. 5194/bg-2021-289 Preprint. Discussion started: 11 November 2021 c Author(s) 2021. CC BY 4. 0 License. database at a monthly time-scale. Vegetation status before, during, and after fire was monitored with four optical and microwave variables: EVI, X-VOD, C-VOD, and L-VOD. Data from June 2010 to December 2020 were considered (10. 5 years), except for C-and X-VOD from AMSR-2 which were only available from July 2012.
Acknowledgements E. B. , A. M. , N. J. R. F. , Y. H. K. acknowledge support by  CNES  ( Centre National d'Etudes Spatiales) TOSCA program. SMOS L2 was obtained from  ESA 's DPGS (Data Processing Ground Segment). The authors would like to thank the  European Space Agency (ESA). AMSR-2 data were provided by  Vrije Universiteit Amsterdam (Richard de Jeu)  and  NASA GSFC (Manfred Owe ) (2014),  AMSR2/GCOM-W1  surface soil moisture (LPRM) L3 1 day 25 km × 25 km ascending V001, Edited by  Goddard Earth Sciences Data and Information Services Center (GES DISC)  ( Bill Teng ), Greenbelt, MD,  USA ,  Goddard Earth Sciences Data and Information Services Center (GES DISC ), Accessed:  29/07/2020 , doi: 10. 5067/M5DTR2QUYLS2. TWS data were obtained from GravIS web portal, the  Gravity Information Service of the German Research Centre for Geosciences (GFZ).
Each group represents buffaloes that are present at the same time in the same area (Figure  1 ). Remote sensing data: 24 Sentinel-2 satellite images of a complete year, corresponding to one image per month for the two tiles (T35KNV & T35KMV), necessary to spatially cover the entire area, have been downloaded in level 1C (Top Of Atmosphere reflectance and orthorectified images) via the Copernicus Open Access Hub. As no Sentinel-2 images were produced at the time of the telemetry data acquisition, we have chosen images from the year 2018 which is representative of the annual rainfall precipitation measured via Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) compared to the years were the telemetry data have been collected. Only the images with less than 10% of cloud cover have been considered.
Limits of the designed model Only eight buffalo individuals have been monitored by telemetry and, at best, only four individuals were simultaneously recorded within the same area at the same time, thus partially reproducing the dynamics of a herd. Indeed, a buffalo herd is composed of at least 200 individuals in our study area  (Miguel et al. , 2017). Given the few individuals used to calibrate buffalo herd behaviour, proven dynamics such as fission-fusion within buffalo herds  (Wielgus et al. , 2020)  are not reproduced by the model. Despite this limitation, the model has been able to coherently simulate the movement of 200 buffalo individuals influencing each other's direction in relation with surface water availability (c. f. 3. 3). Monitoring more individuals and integrating this data in the calibration process should potentially strengthen the model's capacity to reproduce buffalo herd dynamics.
ACKNOWLEDGEMENTS This work is part of a thesis integrated into the TEMPO (TElédétection et Modélisation sPatiale de la mObilité animale) -https://tempo. cirad. fr/enand is implemented under the framework of the  Research Platform "Production and Conservation in Partnership " -https://www. rp-pcp. org/. The authors thank  Dr. Facundo Muñoz , biostatistician in CIRAD -UMR ASTRE, for his precious help regarding the choice of viable statistical tests to calibrate the model.
Introduction To quantify a variable from a Boolean formula is to eliminate that variable from the formula. This quantification process can be performed either existentially or universally, each leading to a different semantics and a different set of applications. The use of quantification in Boolean logic dates back at least to George Boole's work  (Boole, 1854) , where he used universal quantification to perform some forms of logical deduction. Existential quantification has a particularly intuitive interpretation as it can be viewed as a process of removing from a formula all and only the information which pertains to the quantified variable. Due to this semantics, which is referred to as forgetting  (Lin & Reiter, 1994) , existential quantification has received much attention in AI and database theory, particularly in the management of inconsistent information.
Designed as a precise enough tool to allow for the application of the Rule of Law principles in an objective, in-depth, and transparent manner, the Checklist is, when applied, meant to benefit from the broad involvement of interested stakeholders. 15 he Checklist is neither exhaustive nor final; rather, it aims to cover a series of core elements of the Rule of Law whilst taking into account the diversity of Europe's legal systems and traditions. 16 The Checklist translates five principles of the Rule of Law (legality; legal certainty; prevention of abuse of power; equality before the law and non-discrimination; and access to justice) into concrete questions with the intention of applying these to evaluate and assess the country-specific circumstances of its members. It also offers concrete examples of particular challenges with which the Rule of Law is, at times confronted with, such as claims of corruption and conflicts of interest 17 or the collection of personal data and surveillance.
Preslaughter stress status may also alter relationships between variables. As an example, Kwasiborski et al. [120]  found that the slaughter conditions influenced the relationship between catecholamine levels and ultimate pH. In this study, 24 pigs were selected from a larger dataset to obtain a range of values of Longissimus lumborum muscle glycogen content at slaughter. Pigs had been slaughtered either following mixing and transport to the abattoir the day before slaughter (longer-term stress due to overnight mixed lairage at the abattoir) or without mixing, immediately following transport (shorter-term handling/transport stress immediately preceding slaughter). The slope of the correlation between urinary noradrenaline content and ultimate pH depended on the slaughter method (Figure  7 ).
Acknowledgments: We thank  Dominique Bauchart  and  Declan Troy  for the coordination of the projects Lipivimus ( French Ministry ,  2006-2010 ) and  ProSafeBeef (European Commission 6th Framework Programme   2007-2012 ), respectively, which generated some of the data sets reanalyzed in this work and  Elisabeth Duval  for her contribution to the study on the impact of stress at slaughter on the quality of chicken meat. Mohammed Gagaoua is a  Marie Skłodowska-Curie Career-FIT Fellow  under project number  MF20180029. He is grateful for the funding received from the  Marie Skłodowska-Curie  grant agreement No. 713654.
25 • horizontal resolution, are used to validate the WR classification on a longer time period. ERA5 data are selected at 6 h time steps (00:00, 06:00, 12:00, and 18:00 UTC) and regridded to the CAMS product grid at 0. 75 •. For both of the reanalysis products, daily values are obtained at each grid point as the average of 6h time steps, and daily anomalies are computed by removing the low-frequency (LF) component of the time series, estimated by computing monthly means from daily data and interpolating them to daily time steps using a cubic spline interpolation. The limited coverage of the CAMS reanalysis (15 years) does not allow for a robust definition of the climatological seasonal cycle, which would be too dependent on the interannual variability. Therefore, in order to isolate the synoptic variability alone, the definition of a LF component, accounting for the seasonal cycle and the interannual variability, is preferred and applied to both the reanalysis products.
Global reconstructions of the observed sea surface temperature (SST) are used to investigate the teleconnections controlling the synoptic variability. Data are extracted from the Met Office Hadley Centre HadISST dataset  (Rayner et al. , 2003) , available from 1871 at monthly timescale and 1 • horizontal resolution, and from the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) Version 5 dataset  (Huang et al. , 2017) , available from 1854 at monthly timescale and 2 • horizontal resolution.
In situ aerosol observations Observed daily values of the AOD at 500 nm from AERONET stations (https://aeronet. gsfc. nasa. gov/,and transition rates in Tables S1-S3 last access: 10 October 2020) are used to validate the synoptic characterisation performed on CAMS data. Stations are selected among the ones with at least 2 years of level-2 data obtained from the Version 3 Direct Sun algorithm  (Giles et al. , 2018). Stations are selected outside the source region in tropical Africa, namely south of 20 • S and west of 10 • E (Fig. 2a , Table  1 ), in order to focus on BBA transport only, i. e. not being influenced by the BBA emission which is assumed not to be directly related to synoptic conditions. Among the available stations, St. Helena (15. 9 • S, 5. 7 • W) and Wits University (26. 2 • S, 28.
Aerosol synoptic characterisation The WR classification is used to characterise the observed AOD data from the AERONET stations in the region (Table 1). Two approaches are used: 1. Daily AOD anomalies are linked to the corresponding WR and grouped, and statistical differences among groups are investigated (circulation-to-environment approach, C2E). The significance of the C2E characterisation is assessed by a one-way analysis of variance (ANOVA) with the null hypothesis that the distributions associated with each WR are derived from populations with the same mean. Furthermore, for each WR the  significance of the associated AOD anomalies with respect to the full sample is assessed by a non-parametric Kolmogorov-Smirnov (KS) test. 2. Daily AOD anomalies are divided into quartiles, and the changes in the WR occurrences within each quartile are studied (environment-to-circulation approach, E2C).
Synoptic characterisation of aerosol optical depth in situ observations The robustness of the synoptic characterisation of the BBA transport obtained from the CAMS data is assessed by linking the WR classification to the observed AOD from AERONET stations in the region. It is highlighted that data availability and coverage in most of the stations are limited (see Table  1 ), resulting in circa 20-40 observations per WR on average. Only the station on Ascension Island covers the whole period analysed, providing more than 600 observations, i. e. circa 100 observations per WR on average. The C2E characterisation of the AOD observations is presented in Fig. 10. AOD anomalies above Ascension Island show significant negative values during SA-(Fig. 10a ). The significance of this characterisation is confirmed by the ANOVA with a level of confidence higher than 99 %.