dataesr/scientific-paragraphs-categorization huggingface dataset:
We focused our analysis on the eukaryotic diversity by discarding any prokaryotic, plastidic ASVs, or any other artifactual ASV. We used taxonomic annotations obtained with the SILVA database to discard prokaryotic ASVs and the annotations obtained with PR 2 to discard organelle-derived ASVs. All ASVs that only loosely match any V9 reference sequence (i. e. , <20% similarity) were considered as non-18S V9 sequence and were discarded. We also filtered ASVs that did not contain the "GTCG" motif in the first Downloaded from https://www. science. org on February 08, 2022 four nucleotides in the 5′ end. This motif is widely conserved across eukaryotes, whereas prokaryotes have a "GTCA" motif highly conserved in those positions. We lastly used the length distribution of eukaryotic and prokaryotic ASVs to discard any possible prokaryotic unassigned ASVs (filter set at 116 bp; fig.
Classifying eukaryotic ASVs into pelagic or benthic taxa We considered the ASVs being detected in pelagic samples as planktonic (or nektic), the ASVs detected exclusively in sediment samples as benthic, and the ASVs detected in both pelagic and sediment datasets as sinking plankton [although 29 vertebrates ASVs, comprising most of the nekton, represented ~1. 29% of the sequences detected in both pelagic and sediment samples (table  S3 ), we hereafter refer only to sinking plankton]. However, because multiple benthic groups have meroplanktonic larvae and hence could be detected in pelagic samples, we manually curated the ASVs assigned to metazoans within the sinking plankton fraction, based on their known lifestyles. This was, for instance, the case for some polychaetes, molluscs, echinoderms, or harpacticoid copepods that were "forced" into benthic diversity but not for pteropods that were left in the sinking plankton.
Eukaryotic community diversity and structural analysis For a and b diversity analyses, we used functions of the vegan R package v2. 5-3 (77), unless specified differently. Because the size fractionation of the pelagic samples from the Tara Oceans datasets has a strong effect on a and b diversity measures, we compared eukaryotic diversity patterns across pelagic and benthic realms by considering only the richest nano-(3 to 20 m) and pico-(0. 2 to 5 m) size fractions of pelagic samples. The eukaryotic ASV accumulation curves as a function of sampling effort were computed with the specaccum function with the "random" method. We calculated the Shannon diversity for each sample and compared the distribution of sample diversity across both pelagic euphotic and aphotic with the strictly benthic diversity using the stat_compare_means function of the ggpubr R package v0.
2. 5  (78)  with default settings. For b diversity analysis, we removed samples with less than 1000 reads and discarded ASVs represented by less than 100 reads throughout the dataset. We then normalized the ASV-to-sample matrix with the cumulative sum scaling (CSS) method (  79 ) and computed a Bray-Curtis dissimilarity matrix between pairs of samples. The dissimilarity matrix was used to perform a nonmetric multidimensional scaling (NMDS) ordination on two axes. Sampling depth and absolute latitude variables were fit to the NMDS as smooth surfaces using the ordisurf function. The dissimilarity matrix was also used as input of the adonis function for PERMANOVA models testing for differences between eukaryotic compositional structure between realms (pelagic euphotic, pelagic aphotic, and sediment) and along a gradient of absolute latitude (nested in type of realm and restricting permutations within type of realm with the "strata" option), using 999 permutations.
We used generalized additive models (GAMs) to investigate the possible nonlinear variation of richness and Shannon diversity along gradients of latitude, primary production, and POC export from the surface and reaching the seafloor using the gam function of the mgcv R package (https:// cran. r-project. org/web/packages/mgcv/) and the smoothing parameter set to 3. For b diversity analyses, we used a similar approach than detailed above (CSS-normalized and Bray-Curtis dissimilarity matrix), although here, we did not filter rare ASVs. We used the pcoa function of the ape R package  (80)  to perform a principal coordinate analysis of the Bray-Curtis dissimilarity matrix and calculate the structural variation explained by the first two axis of the ordination. We used the ordisurf and envfit functions to respectively fit the absolute latitude and a selection of environmental variables (seabed variables: salinity, temperature, silicate, nitrate, dissolved oxygen, POC reaching the seafloor, and pelagic variables that connect the surface to the DOS, namely, the primary productivity and the POC export from the surface) to the ordination.
Then, we aggregated the planktonic DNA reads of all sediment samples at the station scale and used random forest models to predict the POC export from the surface and the POC reaching the seafloor in a leave-one-out cross-validation approach. We used the ranger function of the ranger R package  (83)  in a regression mode, growing 300 trees and setting the "mtry" parameter at one-third of the total number of features (number of sinking pelagic ASVs). Linear models were used to measure the performance of predictive models. Last, we used a sparse partial least square regression [mixOmics R package  (84) ] to identify the pelagic ASVs detected in the sediment that are best correlated with the variation of POC export and POC reaching the seafloor and with primary productivity and latitudes. We then focused on the pelagic ASVs that were reported with a correlation coefficient above 0.
Systemic Exposure Dose Calculation Based on an Internal Approach A systematic review was performed to assess the levels of BP-3 exposure in the European general population using the search engine Embase. com (Amsterdam: Elsevier) to gather all available human biomonitoring studies. Articles were qualitatively considered for compiling the BP-UV filter exposure database with European exposure data. Selected articles were then quantitatively screened for meta-analysis of BP-3 exposure in the Eu-ropean general population. As older studies are considered less relevant for the current situation, only data after 2006 were included. The analysis included only European studies that applied enzymatic deconjugation of the samples and storage at <4 • C, as these are generally considered critical aspects of sample treatment to obtain reliable and comparable results  [11].
After study selection based on the criteria, the overlapping cohorts amongst the multiple included articles were removed. In filtering out the overlapping cohorts, studies with larger sample sizes, more detailed data reporting, and analysis of multiple benzophenones, were prioritized. The risk of bias of the individual studies was evaluated and studies were excluded from the meta-analysis in case of a considerable risk of bias. The summary statistics were calculated for a typical case exposure and a reasonable worst case exposure. The typical case exposure is described by the median and range (min-max) of median values from the included studies and the reasonable worst-case by the median and range (min-max) of P95 values. Descriptive statistics that were below LOD or LOQ were replaced with the corresponding LOD or LOQ value, conforming to the statistical analysis of the CONTAM panel  [12].
Parameter Value Source Animal PoD NOAEL = 67. 9 mg/kg bw/day SCCS opinion (2021)  [2]  Overall AF AF = 100 ECHA R. The internal exposure was assessed with a meta-analysis of the biomonitoring data. Screening and selection resulted in 17 articles with biomonitoring data on BP-3 in the European cohorts. After application of the exclusion criteria and selection of the studies that used sufficient numbers of samples (>120), 8 studies were selected. Out of these, only 3 studies that had used creatinine correction were considered in the meta-analysis  [23] [24] [25]. Table  4  presents the cohort characteristics and study-specific descriptive statistics of creatininecorrected concentrations of BP-3 and BP-1. Frederiksen et al. (2013)   [25]  reported BP-3 measurements in first morning urine samples of mother-child pairs in Denmark.
Acknowledgements This work was supported by the project  BIOCAP  ( ANR-13-BS06-0004 ). This work on the long-term analysis of the cloud water chemical composition was supported by the  French Ministry and CNRS-INSU. Authors acknowledge additional financial support from the  Observatoire de Physique du globe de Clermont-Ferrand (OPGC) , from the  Auvergne Regional Council , and from the  Fédération des Recherches en Environnement  through the  CPER Environnement  founded by  Région Auvergne-Rhône-Alpes , the  French ministry , and the  FEDER  from the  European community. The authors also thank the  I-Site CAP   20-25. M. Vaïtilingom  and  M. Joly  are gratefully acknowledged for helping in cloud sampling.
Partial 16S rDNA sequences of these three strains were completed with metagenomic contigs and full-length 16S rDNA sequences were submitted to GenBank under accession numbers: OK396682, OK396683, OK396702, and OK396703. About 10 ml of the microcosm was sampled in February 2019 (between day 148 and 163 in Figure  1 ) and used for DNA extraction using a modified CTAB protocol  (Winnepenninckx et al. , 1993) , concentrated to 0. 043 ml (final concentration 0. 03 mg ml - 1  ), and sequenced with the Miseq Illumina technology (2 × 300 bp PE) on the bioenvironnement sequencing platform of the University of Perpignan (France). The 19. 3 10 6 PE reads were trimmed with TrimGalore with options -length 100 -paired, 1 and the resulting 10. 6 Gbp of DNA sequence was assembled with metaSPAdes version  (Nurk et al.
, 2017)  with parameters -k 55,77,99,127 meta. Scaffolds with 95% nucleotide identity over 1 kb BLASTN alignments with nuclear  (Blanc-Mathieu et al. , 2014)  and chloroplastic or mitochondrial genomes  (Blanc-Mathieu et al. , 2013)  of O. tauri were discarded from further analyses. A total of two anchor datasets were built to screen the assembly. First, the reference dataset SILVA_138. 1_ SSURef_NR99  (Quast et al. , 2013)  was used to identify 16S rDNA containing contigs, and the complete 16S rDNA sequence was annotated with RNAmmer  (Lagesen et al. , 2007). Second, the reference genes and corresponding amino acid sequences involved in the adenosylcobalamin (vitamin B12), and biotin and niacin pathways were compiled from  Warren et al. (2002) ;  Helliwell et al. (2016), and Cooper et al.
(2019)  and the Uniprot Knowledge Database  (Boutet et al. , 2007)  and are listed in Supplementary Table  1. The presence or the absence of a gene was inferred from best BLASTN (16S rDNA) or TBLASTN (protein-coding genes) hit from the reference gene set onto the assembly with an e-value threshold <10 -5. The complete assemblies (available on 01/10/2021) of bacterial genomes that belong to the genera identified from the 16S rDNA were downloaded from GenBank: 86 Roseovarius, 75 Winogradskyella, 30 Balneola, and 68 Muricauda. Each contig from the metagenome was affiliated to the genus of the best blast hit (BBH) against this bacterial assemblies by BLASTN (e-value threshold < 10 -5 )  (Altschul et al. , 1990). The coverage of each contig was estimated by aligning the trimmed PE reads onto the assembly with BWA (bwa-mem2-2.
R B : reproductive rate of bacteria throughout the entire experiment. C B : Average daily change in the concentration of bacteria throughout the entire experiment (10 6 cells ml -1 day -1 ). SD is not provided as there was only one replicate. The subindex (1 or 2) indicates whether the estimate corresponds to the time interval before (1) or after (2) the breakpoint. Intrinsic reproductive rates (R 1 and R 2 ) were estimated by the antilogarithm of the slopes. Confidence intervals of 95% are shown within brackets. Asterisk indicates that the 95% confidence interval does not overlap with the confidence interval estimated for axenic O. tauri. Winogradskyella was extracted from the metagenome assembly and also the 16S rDNA sequence of two additional lineages: Muricauda and Balneola. Interestingly, and without surprise, the BBH of these 16S rDNA sequences against GenBank has all been sampled from the marine environment, which includes a strain isolated from the culture of a diatom microalgae (Table  4 ).
Taxonomic affiliation of the metagenome onto available assemblies assigned to these four bacterial genera led to 3. 1 (Winogradskyella) to 4. 8 Mb (Muricauda) MAG assemblies (Table  5 ). The MAG coverage and GC content statistics clearly separated Roseovarius (60% GC) and Winogradskyella (35% GC) affiliated contigs to the Muricauda + Balneola cluster (Figure  6 ). The Roseovarius MAG assembly shared a very high sequence identity (>99. 9% nucleotide identity over >500 kbp) with a genome assembly affiliated to R. mucosus strain 85A, which has been isolated from the culture of a diatom microalgae, whereas the MAGs affiliated to Winogradskyella, Balneola, and Muricauda shared up to 86% nucleotide identity with sequences available from GenBank (Table  5 ). The percent of reads affiliated to each genera is thus 57% to Roseovarius, 39% to Winogradskyella, 1% to  Balneola, and 2% to Muricauda (Table  5 ).
ACKNOWLEDGMENTS We are grateful to all Genophy group members for stimulating discussions, especially  Nigel Grimsley  and  Hervé Moreau , and to  Mathieu Chynel  for starting the initial microcosm experiment in triplicate. We would like to thank the GenoToul bioinformatic platform for access to the computing facilities and the  BIOPIC  and  BIO2MAR  platforms for access and support to the cytometry and molecular biology facilities. We would also like to acknowledge the long-term work of many people involved in the SOMLIT (https://www. somlit. fr/)national monitoring network.
FUNDING SV and LFB were funded by the  EU   Horizon 2020 Research and Innovation Programme , under the  Marie Skłodowska-Curie  grant agreement no. H2020-MSCA-ITN-2015-675752. CC was funded by the  EU   Horizon 2020 Marie Skłodowska-Curie program  ( H2020-MSCA-IF-2020 ) under grant number  101030734.
DATA AVAILABILITY STATEMENT The original contributions presented in the study are publicly available. This data can be found here: partial 16S rDNA sequences of these three strains were completed with metagenomic contigs and full length 16S rDNA sequences were submitted to GenBank under accession numbers: OK396682, OK396683, OK396702, and OK396703. Metagenome Assembled Genomes of the microbiome and raw data are available from PRJNA797933.
Data The region of interest is the Pantanal biome as defined by the Terrestrial Ecoregions of the World  (Olson et al. , 2001). Burned area was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 Collection 6 product  (Giglio et al. , 2018) , developed by the National Atmospheric Space Agency (NASA). Derived from the MODIS sensors aboard Terra and Aqua satellites, MCD64A1 is a monthly burned area product at a 500 m spatial resolution from 2001 to 2020. Re-projected GeoTIFF data for South America was obtained from the University of Maryland's fuoco SFTP Server (fuoco. geog. umd. edu). Burned area totals were computed for the Pantanal and 2001 was dropped as it only includes data from the MODIS sensor aboard Terra. Daily maximum surface air temperature (Tmax) values from 1980 to present were obtained for Pantanal by computing the daily maximum of hourly surface temperatures retrieved from the European Centre of Medium-range Weather Forecast (ECMWF) ERA5 reanalysis dataset  (Hersbach et al.
Bias correction Bias correction was performed using a Quantile Delta Mapping (QDM) approach, in order to match Tmax distribution in the RCM realizations to that of ERA5, despite the discrepancies initially observed. The correction is applied both to historical and future scenario runs. QDM is known to perform well when it comes to preserving raw signals, trends and extremes  (Cannon et al. , 2015; Casanueva et al. , 2020). QDM relies on the computation of the cumulative distribution functions (CDF) of the variable of interest, in the dataset of reference (here ERA5), and in the model to be adjusted on the historical and future periods (here CORDEX-CORE historical and RCPs). Based on these statistical distributions, the transformation applied can be summarized in Eq. (  4 ). Using this approach, the bias corrected Tmax obtained with Eq.
This large inter-model spread is commonly observed in multi-model analyses of RCMs, in particular in the CORDEX framework for South America (e. g. Feron et al. , 2019). The ensemble is also shown to have a mean bias of 2. 72 • C and 3. 76 • C, and a Pearson correlation coefficient of 0. 68 and 0. 79, for the mean and P90 of Tmax, respectively. For impact studies, the ensemble mean is usually able to properly reproduce the main climatological features of the region, notwithstanding the large variability across individual members  (Teichmann et al. , 2021; Coppola et al. , 2021). Considering this inter-model variability and discrepancies compared to ERA5, Tmax datasets from the CORDEX-CORE runs were biascorrected towards the distribution of Tmax in ERA5. Figure  S1  shows the time series for Tmax of raw CORDEX-CORE historical data and both RCP runs over the 1981-2099 period, and the result after biascorrection.
, 2022). As such, fire activity in Pantanal is also inevitably linked to drought and flood  (Mataveli et al. , 2020; Libonati et al. , 2021; Marengo et al. , 2021a). However, precipitation estimates show large inter-model discrepancies over South America  (Solman et al. , 2013; Falco et al. , 2019; Solman and Blzquez, 2019)  due to the commonly acknowledged shortcoming of RCMs when it comes to capturing precipitation. Accordingly, here the focus was made exclusively on the heatwave-fire connection. Still, large biases were found in temperature estimates by the RCMs and, in order to legitimate the analysis of future heatwaves, the bias observed in CORDEX-CORE historical Tmax data with respect to ERA5 was corrected through QDM. Such an adjustment is required in order to obtain more plausible climate change projections, especially when it comes to extreme temperature-related phenomena  (Iturbide et al.
, 2021). Although in this work the bias correction showed a good performance as evidenced in Table  S1  and Figure  S1 , such approaches to adjust simulation data towards a better match with observations have known limitations and shortcomings. In particular, they can be considered statistical artifacts that do not provide clues on the credibility of the physical processes represented in the model  (Maraun, 2016; Maraun et al. , 2017). However,  Maraun et al. (2017)  recognize that for reasonably well captured physical processes, such as the ones driving the spatio-temporal variability of Tmax, usual bias correction methods work adequately. This is arguably the case here since the distribution of Tmax from ERA5 and from all the CORDEX-CORE models could be successfully fitted to the same class of theoretical distribution.
Acknowledgments This collaborative research is the product of a capacity building activity organized by CORDEX-WCRP to promote collaborative activities and networking and to enhance the capacity to document scientific research in Central America and the Caribbean, and  South America  with focus on specific regional climate phenomena (http: //www. cima. fcen. uba. ar/cordex-2020/). P. S. S. and J. L. G. were supported by the  Portuguese Fundação para a Ciência e a Tecnologia (FCT) ,  Portugal  [grants  SFRH/BD/146646/2019  and  2020. 05198. BD], and this work was funded by the  Portuguese Fundação para a Ciência e a Tecnologia (FCT) I. P. /MCTES  through national funds (PIDDAC) -UIDB/50019/2020. The authors also acknowledge  Maria Laura Bettolli ,  Renata Libonati  and  Carlos C. DaCamara , for their constructive comments on this work.
, 2014; Chandler, 2020). On tropical islands, however, the complexity of the rain-topography relationships (e. g. , successive mountain ridges becoming drier at similar altitude) hinders the direct regression of model parameters on topographic data. An interesting option to overcome the non-uniqueness of topography-rainfall relationships is to interpolate the parameters of the rainfall model in space, in order to let data inform non-stationarity  (Kleiber et al. , 2012; Bennett et al. , 2018). This, however, increases model complexity, and therefore requires training datasets with high spatial resolution, which are not yet available for most target areas  (Benoit et al. , 2021). Finally, the difficulty of accounting for orographic effects in the context of high tropical islands is further increased by the temporal variability of rainfall patterns (e.
Rainfall features of interest Because stochastic rainfall models are data driven, their structure depends on the rain features one wants to reproduce in simulations. Hence, identification of the main features of daily rainfall on high tropical islands is a prerequisite for the design of the present model. For illustration purposes, we focus throughout the main text on the island of O'ahu, Hawai'i (lon = 158 • W, lat = 21. 5 • N, area = 1545 km 2 , max altitude = 1220 m). The available rain gauge observation dataset consists of daily records from a network of 86 rain gauges spread over the island (Fig. 1a ), and covers a 20year period 1991-2011. It corresponds to a compilation of quality-controlled and gap-filled daily observations  (Longman et al. , 2018). Gap filling was performed using the normal ratio method  (Paulhus and Kohler, 1952) , and only stations with less than 5 % gap-filled data are kept for this study in order to minimize the impact of gap filling on our results.
To contextualize the observed rain patterns, several meteorological covariates (e. g. , pressure, temperature, humidity, and wind) are investigated at the island scale. We use the ECMWF Reanalysis v5 (ERA5) dataset  (Hersach et al. , 2018)  at 00:00 HST to inform these covariates and average the values of the 12-grid cells (pixel size = 0. 25 • × 0. 25 • ) encompassing the island of O'ahu. Figure  1  displays the main features of rainfall over the island of O'ahu. It shows the strong impact of trade windinduced orographic rain enhancement on the spatial distribution of annual precipitation (Fig. 1a ), with windward (northeast) sides significantly wetter than leeward (southwest) ones, and highlands generally wetter than lowlands. Note some important details of this annual rainfall pattern, for example that the rain maximum is observed leeward of the main crest of the Ko'olau range, and that the Ko'olau mountains are significantly wetter than the Wai'anae range, despite higher elevation.
Model calibration The model is calibrated from a training dataset made of N days of rain accumulation recorded by a network of N T rain gauges (Fig. 1a ). Data must be available for all stations and all days of the calibration period, and a preliminary gap-filling step is required in case of incomplete data  (Longman et al. , 2018; Oriani et al. , 2020). Once a complete training dataset is available, the first step of model calibration consists of inferring the parameters of the transform function (ψ) for each day of the training period. This is performed by calculating the proportion of dry gauges and then estimating the parameters of the gamma distribution of the wet gauges using a maximum likelihood approach. Once the three parameters of ψ are known, this function can be inverted to derive the latent values at each gauge location.