dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements JL acknowledges funding from  IMT Nord Europe  through a post -doctoral fellowship  in the frame of the  IRAPAQ  project. TS acknowledges support from " Institut National des Sciences de l'Univers" of CNRS , and the  LEFE (Les Enveloppes Fluides et l'Environnement)   program  through funding of the  EmAPI  project. The authors acknowledge  Dr Florent Caron  and  Prof. Frédéric Thévenet  ( IMT Nord Europe ) for insightful discussions on atmospheric simulation chambers, and  Dr Joël de Brito  ( IMT Nord Europe ) for interesting discussions about SOA formation. We are grateful to  Mr. Jean-Etienne Régniez ,  Mr. Benjamin Choisel , and  Mr. Benoit Varoqui  ( Mairie de Douai ), for their assistance in the identication and characterization of the asphalt mixture samples, and to  Mr.
Numerical results at next-to-leading order (NLO) for the jet-based TEEC function were obtained  [43]  by using the Nlojet++ program  [44, 45] , which provides the LO and NLO calculations for three-jet production. Furthermore, numerical results for the hadron-based TEEC function at next-to-leading order plus next-to-next-toleading-logarithmic (NLO+NNLL) accuracy were computed recently  [46]. The ATLAS Collaboration has presented measurements of the TEEC and ATEEC functions at centre-of-mass energies of √ s = 7 TeV  [47]  and √ s = 8 TeV  [48] , determining α s as a function of the interaction scale and testing the running of α s predicted by the renormalisation group equation (RGE). The existence of new coloured fermions would imply modifications to the QCD β-function  [49, 50]. Therefore, the running of α s is not only important as a precision test of QCD at large scales but also as a test for new physics.
92, where α s is determined. The relative values of the theoretical uncertainties for the TEEC and ATEEC distributions are shown in figure  5  for the inclusive H T2 bin. The effect of varying α s (m Z ) within the range 0. 117-0. 119 is also shown for illustrative purposes. Figures  6  and 7  show the ratios of the data, as well as of the LO and NNLO theoretical predictions to the NLO predictions for the TEEC and ATEEC, respectively. The description of the data provided by the NNLO pQCD predictions is very good, improving with respect to the NLO prediction. In these figures, the distributions are rebinned to reduce statistical fluctuations in the theoretical predictions that, despite the high statistics, can be present due to insufficient precision on the cancellation of the different terms.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech Republic; DNRF and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRI, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;.
Model data One ensemble of 13 global climate models (GCMs) following the CMIP6 protocol  (Eyring et al. , 2016)  is considered. This selection of models is listed in Table  1. To define compound wind and precipitation extremes, we use daily precipitation and wind speed maxima variables. For growingperiod frost, mean and minimum temperature variables are used. For each variable, the historical period simulations (1871-2014) have been extracted and extended until 2100 using the shared socioeconomic pathways 585 (SSP-585) scenario  (Riahi et al. , 2017). Since the 13 selected simulations present different spatial resolutions, each climate simulation dataset has been regridded to a common spatial resolution of 0. 5 • × 0. 5 • using bilinear interpolation. Considering the climate models separately will allow us to assess inter-model variability in terms of ToE of CE probabilities, as well as the potentially different contributions of marginal and dependence properties to changes in probability of multivariate climate hazards.
g. Cannon, 2018; Vrac, 2018; François et al. , 2020). This could have major consequences on the ability of climate models to simulate CEs accurately  (Zscheischler et al. , 2019; Villalobos-Herrera et al. , 2021; Vrac et al. , 2022a; Ridder et al. , 2021) , and then on the resulting analyses involved in decision-making processes. A few multivariate bias correction methods, i. e. statistical methods that are able to adjust both univariate and multivariate properties of simulations with respect to reference datasets, have been recently developed (e. g. Cannon, 2018; Guo et al. , 2019; Mehrotra and Sharma, 2019; Robin et al. , 2019; Vrac and Thao, 2020; François et al. , 2021). However, such MBC methods are designed to adjust the whole statistical distribution of climate simulations, and their abilities to increase the realism of specific parts of the statistical distribution (such as multivariate extremes) have never been tested, while this can be crucial for specific CEs.
Acknowledgements. We acknowledge the  World Climate Research Programme 's  Working Group on Coupled Modelling , which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1 of this paper) for producing and making available their model outputs. For CMIP, the  US Department of Energy's Program   for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure  in partnership with the  Global Organization for Earth System Science Portals. Financial support. The authors acknowledge support from the  EUR IPSL Climate Graduate School  project managed by the  ANR  under the " Investissements d'avenir" programme  with the reference  ANR-11-IDEX-0004-17-EURE-0006 , the  European Union 's  Horizon 2020 research and innovation programme via  the " XAIDA" project  (grant no.
Code availability. Custom codes developed for the analyses are publicly available at https://doi. org/10. 5281/zenodo. 7509302  (François and Vrac, 2023). Data availability. CMIP6 climate model data can be downloaded through the Earth System Grid Federation portals. Instructions to access the data are available here: https://pcmdi. llnl. gov/CMIP6/ Guide/dataUsers. html (last access: 23 January 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-23-21-2023-supplement.
Recently, a number of studies have indicated that the 16S rRNA gene (which is found in the chloroplast genomes of eukaryotic phytoplankton, as well as in bacterial genomes) has a more constrained copy number in eukaryotes than does the nucleusencoded 18S rRNA gene  (Needham and Fuhrman, 2016; Choi et al. , 2020). This makes data from the 16S rRNA gene marker potentially closer to representing shifts in cell abundance than the 18S rRNA gene marker. Here, we analyzed the V1-V2 hypervariable region of the 16S rRNA gene (16S V1-V2) from salt pond, saltern, mangrove, above-reef, and farther offshore microbial communities in samples collected annually from 2015 to 2019. In addition to examining microbial diversity and potential co-occurrence of ASVs from different microbial (i. e. , including phytoplankton) taxa, ASVs from eukaryotic phytoplankton (plastid-derived) and cyanobacteria were used to characterize the phytoplankton community.
A selection of above-reef and off-reef samples was also sequenced for the V4 hypervariable region of the 18S rRNA gene (18S V4) to compare with 16S V1-V2 amplicon data and connect to literature on eukaryotic phytoplankton species that have previously been defined using 18S rRNA gene phylogenies. Collectively, our study provides insights into how phytoplankton diversity and community composition change along the tropical inlandoffshore habitat gradient.
Nutrient samples were measured using an AlpKem autoanalyzer according to protocols from  Sakamoto et al. , 1990. Salinity was measured using a hand refractometer from 2016-2019. Additionally, the sampling method used in 2019 did not allow for measurements above 100‰; therefore, for the hypersaline samples, the exact value was not measured but was considered above the range in which quantification is possible. Temperature throughout the day was measured for CARMABI/Piscadera Bay mangrove and reef in 2017 and 2019, Spanish Bay salt pond, mangrove, and reef in 2019, Sint Michiel and Sint Willibrordus salterns in 2019, and Water Factory reef in 2019 using a HOBO data logger (Onset Brands, Bourne, Massachusetts). Temperature data for open ocean sites was retrieved from global daily 5 km satellite data from the National Oceanic and Atmospheric Administration Satellite and Information Service.
, 2020) , were then placed on a Viridiplantae reference tree in a second classification step to assign the final taxonomy of these ASVs. ASVs assigned to stramenopiles in the plastid and cyanobacteria placement were re-classified in a multi-step approach using reference trees as described in  Choi et al. , 2017; Choi et al. , 2020. A subset of green algae could not be placed at terminal nodes using PhyloAssigner, and therefore were manually checked. In the case of salt pond and saltern chlorophyte ASV placements, they were used as blast queries against GenBank nr and found to be Dunaliella. Mangrove, salt pond, and saltern diatom ASV placements were further investigated using them as queries against GenBank nucleotide database. Amplicons from Micromonas clades A and B can be delineated from Clades C, D, and E sensu S ̌lapeta et al.
, 2010). Paired-end library sequencing (2×300bp) was performed on PCR products and PCR negative controls using the Illumina MiSeq platform. Sequence data were trimmed (forward reads to 220 bp and reverse reads to 200 bp), dereplicated, checked for chimeras, and assigned to ASVs using the DADA2 R package, version (v1. 10. 0). Post quality control this rendered 287,092±56,468 18S V4 amplicons per sample. Rarefaction of 18S V4 amplicons was performed as for 16S rRNA gene amplicons. 18S V4 ASVs were initially classified using the sklearn trained classifier and the PR2 database  (Guillou et al. , 2013). The relevant prasinophyte ASVs were then used to connect 18S rRNA gene derived clade information with the 16S rRNA gene (plastid-derived) ASVs that were the focus of our study. Connections were made by comparing relative contributions of specific amplicons/ASVs to all those from the genus in both 16S V1-V2 and 18S V4 (in the same samples).
The potential links between environmental parameters and ASV-level distributions of green algae and chlorarachniophytes were investigated via canonical correspondence analysis (CCA) using the R packages tidyverse, reshape2, and ggplot2  (Wickham et al. , 2019). A Variance Inflation Factor (VIF) analysis was performed in order to check for multicollinearity among variables using the car package  (Fox et al. , 2022). ASV data were normalized using the Hellinger method and both data normalization and CCA were run using the vegan library. The significance of the association of environmental parameters with ASV distribution was determined using the ANOVA-like permutation test for CCA in vegan, anova. cca. Analysis of similarity (ANOSIM) tests were performed on Hellinger-transformed count data of green algal and stramenopile ASVs.
Variations in stramenopile and green algal diversity Stramenopiles and green algae represented the two major eukaryotic phytoplankton groups in the ASV data across the habitats (Figure  3C ). Each displayed distinct community composition among environments when just considering these two major groups independently (ANOSIM test statistic r=0. 64, p<0. 001 for each). When analyzing the stramenopile community, those in mangrove habitats, particularly in Spanish Bay/Spaanse, grouped closer to salterns located at Sint Willibrordus than seen for either the whole community NDMS analysis or that for the photosynthetic community (Figure  S3 ). In contrast, above-reef, off-reef, and open sea communities mostly grouped closely, similar to the other NMDS plots (Figure  S3 ). Differences between coastal and inland Sint Michiel samples were observable, but less pronounced than in the NMDS plots with all ASVs (Figures  S3A, C ).
We next characterized the global averages of different stramenopile groups by averaging the relative amplicon abundances (as percent of total stramenopile amplicons) from each site, using all data collected over the five years regardless of available metadata (e. g. , nutrients, etc. ). We also determined the total number of ASVs found for each group in those five years. In salt ponds, the dominant stramenopiles comprised several diatom lineages, but because diatoms were not present in all samples their average contribution across the samples was highly variable (41±44% relative abundance of all stramenopile amplicons). The most abundant stramenopile ASV (ASV7549) was closest to the pennate diatom Synedra hyperborea (98. 6% nucleotide (nt) identity). The exception to the overall dominance of diatoms occurred in the Spanish Bay/Spaanse salt pond, which had relatively high but variable contribution of eustigmatophytes (maximum 91%, with an average of 26±40% at this site) (Figure  4A ).
Discussion Our study sites spanned a range of distinct tropical island aquatic habitats. In general, these types of environments are under marked anthropogenic influences, both local (van der Schoot and Hoeksema, 2022) and climatic  (Bove et al. , 2022) , and hence it is important to establish coherent relatable data sets for different regions within tropical ecosystems for benchmarking potential downstream changes. There were differences in the level of connectivity to the sea and/or human impact between sites within the same habitat type. For example, among mangroves, those at Playa Santa Cruz were the most remote, while Spanish Bay/Spaanse had more nearby human development, and CARMABI/Piscadera Bay was near the most developed part of the island  (Brocke et al. , 2015). We sampled above the shelf of fringing reef corals, which extend about 70 m from the shore, at CARMABI and the Water Factory located toward the midpoint of the island perimeter (again in a more anthropogenically forced area) as well as East Point, which is less impacted.
A study of the impact of eutrophication on coral reef fauna around Curacao found that sewage run-off was high around CARMABI/Piscadera, Water Factory, and Spanish Bay, while Sint Michiel and sites farther north from that had lower sewage output, as determined using d 15 N isotope data (van der Schoot and Hoeksema, 2022). Our off-reef samples were collected just past the reef above the 1000 m trench, while the open sea samples were from ~25 km away from shore (Figure  1 ), both presumably being less impacted by human activities. We also delineated two types of salt ponds based on the level of anthropogenic modification, the first being salterns (Sint Willibrordus, Sint Michiel inland and coastal, and Jan Thiel) which have undergone construction of retaining walls to isolate areas for evaporative salt harvestingbut with the former two sites having inflow of fresh salt water in order to replenish the system.
Acknowledgments We thank  CARMABI  and  CARMABI  staff for providing lab space, residential space and for facilitating field work, as well as  P. Keeling  and  F. Rohwer  for stimulating and organizing initial Curacao field trips. We are grateful to  K. Hoadley ,  J. Grone ,  R. Spezzano  and other Worden lab members for support, as well as the  Monterey Bay Aquarium Research Institute (MBARI).
Several observations and reanalysis datasets were considered. Time series of in situ measurements at meteorological stations over the airports from the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climatology Network (GHCN)-Daily dataset  (Menne et al. , 2012b) w ereused. The NOAA GHCN-Daily dataset contains a large collection of meteorological series from land stations worldwide, and it is the result of international agreements to exchange climate data. Concerning Europe, the European Climate Assessment and Dataset project  (ECA&D; Klein Tank et al. , 2002)  contributes to this initiative and provides data from a large number of European stations, with most of the observational records starting in 1961. All the data collected and merged for the NOAA GHCN-Daily dataset undergo a quality-control assessment that is common for all the measurements.
This dataset is frequently updated, with at least regular monthly updates for European stations. Here, the latest update of the NOAA GHCN-Daily version 3 was used  (Menne et al. , 2012a) , which was available up to December 2019. The data were acceded via R software using the 'rnoaa' package  (Chamberlain, 2021). E-OBS gridded observational dataset was also analysed  (Haylock et al. , 2008; Cornes et al. , 2018). It is a land-only dataset available over Europe, built on series from stations that are considered in the ECA&D project. The version 24. 0e of the dataset, which is available on a regular grid of 0. 1 × 0. 1 (EOBS 01deg) from 1950 to 2021, was used. The SPAIN02 reanalysis was also considered  (Herrera et al. , 2012). This is a product provided by the University of Cantabria (Spain) based on meteorological series recorded by the Spanish Meteorological Agency (AEMET) over the peninsular Spain and the Balearic islands.
The fifth and latest version of SPAIN02 dataset  (Herrera et al. , 2016; Kotlarski et al. , 2019)  was obtained from the AEMET climate services portal (http://www. aemet. es/es/ serviciosclimaticos/cambio climat/datos diarios?w=2&w2=1). This covers the period 1971-2015 and is available on a regular 0. 1 × 0. 1 grid. Another reanalysis considered for this study is the Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige-France (SAFRAN-France;  Quintana-Segui et al. , 2008; Vidal et al. , 2010). SAFRAN-France is a product of Météo-France, which offers climate data over the Metropolitan France on a 8 km regular grid for the period 1950-2014. Table  1  summarises the observational datasets and reanalysis that are available for each selected airport. Table  2  synthesizes the main characteristics of the datasets described above.
Characterisation of TX extremes and trends The amplitude of extreme events was analysed from quantile-quantile (q-q) diagrams, as in  Herrera et al. (2010)  for precipitation or in  Christensen and Boberg (2012)  for monthly mean temperatures, for instance. In these q-q plots, the quantiles of the different datasets were compared with those of the observational reference dataset. EOBS 01deg was chosen as the observational reference since data are available over all the selected airport locations, and its resolution is nearly the same as that of the RCMs, as they will be compared later. Trends were computed by the quantile regression method, which was first introduced in  Koenker and Bassett Jr (1978) , and further developed in  Koenker (2005)  and  Koenker (2017). This method allows to obtain the linear temporal trends of the TX Probability Distribution Functions (PDFs) by quantiles (or percentiles), thus offering information about the evolution of the PDF shape, and not only about mean changes as the most-commonly used Ordinary Least Squares (OLS) regression method does.
The analysis presented in this study can be organised in five steps. 1. Extreme values and trends of TX in the recent period 1961-2014 are characterized from the observational and reanalysis datasets over the nine selected airports (Table  1 ). Results from the different datasets are compared to estimate to what extent the evaluation of climate models depends on the observational reference choice. Data over the selected airports are extracted from the gridded products once the nearest grid points are identified in each case. 2. The Euro-CORDEX Evaluation experiment is evaluated with respect to an observational dataset in terms of extremes amplitude and the quantile trends of the TX for the period 1979-2008. This analysis allows to estimate the errors that are proper to RCMs. The land-sea mask corresponding to each RCM is taken into account to select the nearest grid points to the airport locations.
5 C and 1. 7 C. Positive trends found for all the airports are coherent with the rise in heat events in the Euro-Mediterranean region in recent decades  (Stocker et al. , 2013; Masson-Delmotte et al. , 2021). These airports enlarge the list of vulnerable airports to global warming considered in  Coffel and Horton (2015) ,  Coffel et al. (2017) ,  Zhou et al. (2018)  and in  Gratton et al. (2020) , including major Euro-Mediterranean airports. The EOBS 01deg dataset shows a very similar behavior to that of the NOAA GHCN-Daily dataset, for both extreme values and trends. This is probably explained by the fact that the nearest in situ stations to the selected airports that the EOBS 01deg dataset integrates are also included in the NOAA GHCN-Daily dataset. SPAIN02 and SAFRAN-France reanalysis show large discrepancies with respect to the EOBS 01deg and the NOAA GHCN-Daily datasets.
In general, SAFRAN-France almost systematically overestimates the upper quantile trends, except for the TLS airport. For SPAIN02, the reason of the differences from observations can be explained by the sensitivity of trends computation to the period of study (see Table  2 ). Indeed, if we recompute the quantile trends for a common period  for all the datasets at the Spanish airports, SPAIN02 exhibits more similar trends to the EOBS 01deg and the NOAA GHCN-Daily datasets (not shown). Nevertheless, SAFRAN-France reanalysis overestimates by 2 C the 95 th percentile trend in the period of study with respect to the EOBS 01deg and the NOAA GHCN-Daily datasets. To conclude, differences between datasets for TX trends in the observational period can reach up to 4. 0 C, depending on the location and also on the percentile.
However, all of the datasets remain mainly coherent at most airports because of the wide amplitude of the confidence intervals from the quantile trends computation. Also, the maximum difference between datasets obtained for the TX upper percentiles is 1. 4 C, which corresponds to a tolerable bias for temperatures that exceed 35 C. According to these results, we consider that the choice of the observational reference among these datasets may not be determinant for the evaluation of the climate models in terms of the local TX. We consider hereinafter EOBS 01deg as the reference dataset for the evaluation of RCMs and GCMs.
This result highlights the need for computing the temperature trends by quantiles, since TX extreme events are the most problematic for aviation. Although observational datasets exhibit some differences, the maximum divergence concerning the TX upper percentiles is 1. 4 C for temperatures exceeding 35 C, and TX trends are mainly coherent amongst the datasets for most of the airports. Euro-CORDEX RCMs were evaluated by comparing regional climate simulations with an observational reference. First, RCM performances when driven by the ERAI reanalysis were studied using the Evaluation experiment. A systematic overestimation of TX extremes by RCMs was found. As suggested in  Vautard et al. (2013)  and  Vautard et al. (2020) , the overestimation of temperatures by Euro-CORDEX RCMs may be linked to the underestimation of the precipitation in the Iberian Peninsula and the Mediterranean region.