dataesr/scientific-paragraphs-categorization huggingface dataset:
0 License. For the aforementioned boreholes monitoring GWL of chalk aquifers (Pihen-Lès-Guînes, Blacqueville and Grandes-Loges), we also investigated effective precipitation (EP) corresponding to these boreholes. For each borehole, we assigned to it the mesh with EP time series the most explanatory of GWL. This linkage between the borehole and a SAFRAN mesh was carried out prior to this study by correlative analysis. In this study, we used monthly cumulative EP time series over the 1976-2019 period. The meteorological data (precipitation (P), snow, temperature and Penman-Monteith potential evapotranspiration (PET)) from the SAFRAN reanalysis were used as input data to compute effective precipitation. This reanalysis provides daily data on a 8x8km2 mesh covering France from 1958 to 2019  (Vidal et al.
, 2003; Cornish et al. , 2006). While not necessarily essential for signal or image processing or numerical compression, this property is fundamental for physical interpretation of the wavelet details in multiresolution analysis, and has already been used to that purpose in several studies such as  Percival and Mofjeld (1997) ,  Massei et al. (2017) and Pérez Ciria et al. (2019). The dominant frequency associated with each MODWT wavelet detail was calculated by Fourier transform of each wavelet detail. The MODWT also provides the amount of variance (or energy) explained by each wavelet detail and frequency level. The energy percentage of a given wavelet detail expresses the relative importance of this variability in the total signal variability. As a result, the energy distribution between wavelet details for each borehole in the database can be extracted and mapped.
Acknowledgments This work was partially supported by the  GeoERA  project  TACTIC , funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  731166. We would also like to thank the  Agence de l'Eau Seine Normandie, PIREN Seine, BRGM  and  Région Normandie  for their financial support. Finally, we would like to thank  Sandra Lanini  for the calculation of effective precipitation.
Data availability The groundwater level data used for this analysis can be obtained from https://ades. eaufrance. fr/ (last access: 1st April 2020). For the database of relatively undisturbed GWL time series regarding water abstraction, contact us. The Safran precipitation data set can be obtained from https://donneespubliques. meteofrance. fr/ (last access: 1st April 2020). https://doi. org/10. 5194/hess-2022-10 Preprint. Discussion started: 10 January 2022 c Author(s) 2022. CC BY 4. 0 License.
Furthermore, the use of volume reduces the impact of noise, which can occur when processing is based on sky visibility (e. g. , sky-view factor or positive openness) or second derivatives. Finally, the volumetric approach allows the implementation of a vertical exaggeration factor, the result of which will enhance the particular characteristics of the landscape. The present study comes with a standalone executable program for Windows, a QGIS plugin, and the scripts written in Python, including GPU compute capability (via CUDA) for faster processing.
Implementation The three methods presented above were implemented in Python, provided here as an open-source script (SM1), as a standalone executable program for Windows (SM2), and as a QGIS 3. X plugin (SM2). The vSky program uses the following Python libraries: PyQT 5. 9. 2, Numpy 1. 16. 4, Pillow 6. 1. 0, Scipy 1. 2. 1, Cupy 6. 0. 0, and Gdal 3. 0. 2. This code allows any projected DEM to be loaded and processed, provided that the resolution is the same in X and Y. Three parameters are tunable: the radius of the sphere and/or hemisphere influence, the strength of smoothing (which is obtained by applying a Gaussian kernel and fast Fourier transform convolution), and the vertical exaggeration (Figure  4 ). Smoothing and vertical exaggeration optionally pre-process the DEM. The first reduces aliasing and noise by attenuating high-frequency relief, while vertical exaggeration applies a multiplying factor to the altitude, exaggerating or attenuating variations in relief.
Here, PACV was computed using a modified version of the Python scripts made available by  Hu et al. (2021)  at https://github. com/NJNU-DTA/PACV (accessed on October 2020) (Figure  10e ). Interestingly, an equivalent output can be obtained with the VO algorithm, when the DEM is adequately smoothed and exaggerated (Figure  10f ). As PACV is not scale dependent, exaggeration has no effect on its output, whereas VO can be freely tuned using exaggeration for more versatility.
The following supporting information can be downloaded at: : https://gitlab. huma-num. fr/fmonna/vsky/-/tree/Paper_Supp_Mat. SM1: Python open-source script of vSky. SM2: Standalone executable version of vSky for Windows with user guide and DEMs examples as well as the QGIS 3. X plugin of vSky. SM3: Full resolution version of the figure 9. Author Contributions: Conceptualization, T. R. and F. M. ; methodology, T. R. , F. M and J. F. B. ; software, F. M. and B. B. ; validation, T. R. , F. M. and J. F. B. ; formal analysis, T. R. and F. M. ; data curation, B. B. ; writing-original draft preparation, T. R. , F. M. and J. F. B. ; writing-review and editing, T. R. , F. M. J. F. B. , J. M. , Y. E. , B. B. and C. C. -S. ; visualization, T. R. ; supervision, T. R. and F. M. ; funding acquisition, F.
Data Availability Statement: ALOS/PRISM AW3D30: https://doi. org/10. 5069/G94M92HB. Acknowledgments: This research was funded by the Joint Mission Mongolia-Monaco and the project ROSAS (uB-FC and RNMSH). The data used for this paper were provided by ALOS/PRISM AW3D30 of the Japan Aerospace Exploration Agency and the joint Mission Mongolia-Monaco. We are grateful for comments by the anonymous reviewers, which have greatly improved the manuscript.
Supplementary Materials: The following supporting information can be downloaded at: : https://gitlab. huma-num. fr/fmonna/vsky/-/tree/Paper_Supp_Mat. SM1: Python open-source script of vSky. SM2: Standalone executable version of vSky for Windows with user guide and DEMs examples as well as the QGIS 3. X plugin of vSky. SM3: Full resolution version of the figure 9. Author Contributions: Conceptualization, T. R. and F. M. ; methodology, T. R. , F. M and J. F. B. ; software, F. M. and B. B. ; validation, T. R. , F. M. and J. F. B. ; formal analysis, T. R. and F. M. ; data curation, B. B. ; writing-original draft preparation, T. R. , F. M. and J. F. B. ; writing-review and editing, T. R. , F. M. J. F. B. , J. M. , Y. E. , B. B. and C. C. -S. ; visualization, T. R. ; supervision, T. R. and F. M.
The age and distribution of deposits from major Holocene eruptions have been studied in the Cordillera, but no descriptions of distal fallouts have been published. In this study, we focused on 28 Holocene tephra layers recorded in marine sediment cores collected along the northern Ecuador-Southern Colombia margin. New lithological, geochemical and isotope data together with 14 C datings on foraminifers allow us to determine the age and volcanic source of marine tephra, and to propose a first land-sea correlation of distal tephra fallouts. We show that at least seven explosive eruptions from Guagua Pichincha, Atacazo-Ninahuilca, Cotopaxi, and Cerro Machín volcanoes left tephra deposits recorded in marine cores over 250 km away from their source. Volume estimates of emitted tephra range between 1.
Scanning Electron Microscope The size and morphology of glass shards, as well as any mineral inclusions, were assessed by scanning electron microscopy (SEM) at the Université Côte d'Azur (Nice, France). SEM observations and energy dispersive X-ray spectroscopy (EDX) analyses were carried out with a Tescan Vega3 XMU SEM (Tescan France, Fuveau, France) equipped with an Oxford X-MaxN 50 EDX detector (Oxford Instruments, Abingdon, UK) at accelerating voltages of 10 kV (imaging) and 20 kV (EDX). EDX spectra were processed with the Aztec software (version 3. 2, Oxford Instruments). Samples were mounted on a SEM stub and carbon-coated prior to observations. SEM micrographs are given in Data Set S2 and EDX data in Data Set S3.
10. 1029/2022GC010466 15 of 38 samples no longer showed any apparent carbonates. A magnetic separator was then used to remove the possible xenoliths. Agate-crushed powders of bulk glass shards with scarce non-magnetic phenocrysts such as plagioclase or quartz were measured at the Laboratoire Géosciences Océan of the Université de Bretagne Occidentale (Brest, France), following the procedure detailed in  Cotten et al. (1995). Relative uncertainties are ≤5% for most trace elements. Minor and trace element contents are presented in Data Set S3. Ni, Y, and Yb contents measured by LA-ICP-MS on single glass shards were often under the detection limit. As ICP-AES measurements performed on bulk tephra samples are in agreement with the maximum contents measured by LA-ICP-MS for these three elements, we used ICP-AES for the latter and elements that were not analyzed by LA-ICP-MS (indicated in yellow in Data Set S3).
Geochemistry Major element contents of each tephra layer determined using electron microprobe on single glass shards are given in Data Set S3 and K 2 O versus silica contents are plotted in Figure  4a. Marine tephra samples belong to the medium-and high-K calc-alkaline magmatic series, with K 2 O contents ranging between 1. 9 and 4. 0 wt. %. All samples display a homogeneous rhyolitic composition with SiO 2 contents   ). Numbers: sample name (AMAD-xx or ATAC-xx). HK, MK, LK: high-, medium-, and low-K calc-alkaline series. Two points for ATAC-55 and ATAC-56 samples illustrate their heterogeneous composition, made up of a mixture between andesite and rhyolite glass shards. Small symbols correspond to analyses performed onland on major Holocene eruptions from Atacazo-Ninahuilca (whole rock on pumices and blocks from pyroclastic flow deposits,  Hidalgo et al.
(1990) ; and whole rock on scoria and pumices from the 1985 AD eruption,  Gourgaud and Thouret (1990) ,  Sigurdsson et al. (1990), and Melson et al. (1990) , for Nevado del Ruiz volcano). (b) MgO/FeO versus Al 2 O3/Na 2 O diagram of marine tephra layers. Each point represents average of single shards measurements performed by Electron Microprobe (Data Set S3). Tephra layers with similar composition are represented by the same color, which is also used in the following figures. higher than 74. 8 wt. % (standard deviations are given in Data Set S3), except for samples ATAC-55 and ATAC-56 (core KAT20), which also present andesite glass shards, with SiO 2 contents of ∼62 wt. %. This diagram reveals groups of tephra of similar composition, which are distinguished by their alkaline and silica contents (Figure  4a ), but also by their MgO/FeO and Al 2 O 3 /Na 2 O ratios (Figure  4b ).
Identification of the Source of Marine Tephra Layers Based on Their Major and Trace Element Contents and Sr-Pb Isotope Ratios Measurements of major and trace element contents of marine tephra have been performed in this study on single glass shards. Very few measurements on single glass shards of Holocene tephra deposited onland in Ecuador and Colombia have been published. We have thus mainly compared our results with bulk tephra or whole-rock data obtained on pumices, blocks from pyroclastic flows, lava flows or scoria clasts (GEOROC database; http:// georoc. mpch-mainz. gwdg. de/). Comparison of these two data sets should therefore be considered with caution, as the element contents of pristine glass and whole rock material can significantly differ depending on the mineral contents of the latter.
Tephra Dispersal and Size of Major Holocene Eruptions Our correlation of distal tephra samples to dated Holocene eruptions in the Ecuadorian Cordilleras allows us to map the sites where each tephra isochron is recorded in distal sediments. The resulting maps reveal a NW distribution for the 980 cal BP Guagua Pichincha tephra deposit, consistent with the plume direction inferred from thickness data in proximal areas (Figure  9a ). The tephra fall covers 2. 8 × 10 4 km 2 within the 1 cm isopach, and tephra have been transported until 250 km from their source. Distal deposits from other large Holocene eruptions (Ninahuilca 4910 cal BP and two events from Cotopaxi) display westerly dispersal patterns that are also supported by proximal thickness data at both volcanoes (Figures  9b-9d ). This is a typical situation in the Ecuadorian region where prevailing winds of the Inter-Tropical Convergence Zone blow from the Amazonian lowlands toward the Pacific Ocean, that is, from East to West.
We determined the area of the latter using the ArcGIS software (Table  S1 ) and used the single isopach approach of  Legros (2000)  to infer the minimal tephra volume of each layer, which ranges from 0. 4 to 5. 6 km 3 (Figure  10a , details in Table  S1 ). To examine the general thickness decay rate of the tephra layers, we merged our data with those of the literature in a classical plot Log(thickness) versus (isopach area) 1/2 (Figure  10b ). We obtain a best fit for our data sets using an exponential thinning rate with two or three breaks-in-slope (Figure  10b ;  Bonadonna & Houghton, 2005). Results obtained for Guagua Pichincha and Atacazo-Ninahuilca deposits (1. 3 and 2. 1 km 3 , respectively) are slightly higher than previous estimates based on proximal deposits. In contrast, tephra volumes obtained for Cotopaxi tephra layers (5.
(2017)), and Colombian volcanoes (whole-rock on undated scoria clasts, pumice and lavas for Azufral, Cerro Machín and Galeras volcanoes, Marriner and Millward (1984) , Droux and Delaloye (1996) ,Calvache and Williams (1997), Laeger et al. (2013) , Ancellin et al. (2017), and Errázuriz-Henao et al. (2019) ; single glass shard from the 1985 AD pyroclastic deposits, Calvache (1990 ), Vatin-Pérignon et al. (1990) ; and whole rock on scoria and pumices from the 1985 AD eruption, Gourgaud and Thouret (1990) , Sigurdsson et al. (1990), and Melson et al. (1990) , for Nevado del Ruiz volcano). (b) MgO/FeO versus Al 2 O3/Na 2 O diagram of marine tephra layers. Each point represents average of single shards measurements performed by Electron Microprobe (Data Set S3). Tephra layers with similar composition are represented by the same color, which is also used in the following figures.
Drawing on the GBD 2019, the aim of this study was to provide the most comprehensive report to date on ASD global prevalence and disability across the past 30 years. Assessing trends in diagnostic rates is particularly relevant in the light of concerns on the possible impact on administrative prevalence and incidence driven by changes in nosographic systems. For instance, changes in the diagnostic criteria of ASD from the Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV to DSM-5 have led to a decrease of 13-20% in its estimated prevalence within the same dataset. [20, 21]  Some experts suggest the DSM-5 criteria are more stringent than DSM-IV,  [22]  and some children meeting the criteria under the DSM-IV-TR pervasive developmental disorder (PDD) will not meet DSM-5 ASD criteria but might meet the social communication disorder (SCD).
Data source The GBD 2019 Results Database is publicly available through the Global Burden of Disease Collaborative Network website (http://ghdx. healthdata. org). In GBD 2019, ASD is defined based on DSM (III, III-R, IV, IV-TR, 5), ICD  (9, 10) , Chinese Classification of Mental Disorders (CCMD), or diagnosed by a clinician using established tools. Additional information on the methodology is available in the supplementary methods in the appendix (Tables S1-S4, Figures  S1-S8 ). [18]  GBD 2019 estimates provide information about incidence, prevalence, mortality, years of life lost (YLLs), YLDs, and DALYs related to 369 diseases and injuries. [18]  Data are provided for 204 countries and territories, grouped into 21 regions and seven super-regions, and extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources.
GBD collects data from a total of 86,249 sources, including 19,354 sources reporting deaths, 31,499 sources reporting incidence, 19,773 sources reporting prevalence, and 26,631 sources reporting other metrics. Diseases and injuries analyzed by the GBD are organized into four levels of hierarchy from Level 1 to Level 4. Level 1 includes three broad causes for death and disability (i. e. , communicable, maternal, and nutritional diseases; non-communicable diseases; and injuries). With increasing granularity, Level 2 includes 22 different causes, Level 3 encompasses 174 different causes, and Level 4 provides the most specific estimates related to 301 specific causes. [18].
Statistical analyses GBD assembles clinical informatic data including hospital data, ambulatory (including general practitioner) visits, and health insurance claims. For each GBD causes (diseases), ratios of non-primary to primary diagnosis rate, and ratios of outpatient to inpatient care are extracted from several regions. [18]  The log of the ratios are modelled by age and sex using MR-BRT (Meta-Regression-Bayesian Regularised Trimmed), the Bayesian meta-regression tool. [18]  GBD uses three main modelling strategies including Cause of Death Ensemble model (CODEm), Spatiotemporal Gaussian process regression, and DisMod-MR 2. 1 to generate estimates of each measure of interest by age, sex, location, and year. [18]  Details of each model are provided in Supplementary methods.
Also, we do not report prevalence, incidence and burden of ASD by intellectual disability levels, despite the previous report that YLDs of intellectual disability levels constitutes DALY of ASD. [18]  Lastly, multiple studies have shown that the presence of co-occurring problems of ASD including ADHD, could diminish the performance of diagnostic algorithms of ASD. [53] [54] [55] [56] [57]  GBD 2019 took the disability weights for the sequela of ASD for intellectual disability into account, but methods to make dependent comorbidity corrections computationally feasible are still under development. [18]  Current GBD database provides the prevalence, incidence, and YLDs of ASD according to six intellectual disability levels; ASD with no, borderline, mild, moderate, and profound intellectual disability.
Among 167 input sources, only 19 studies that reported information on the IQ level were used in the meta-analysis to calculate the severity splits by intellectual disability. [18]  In the upcoming studies, those sequelae can also be taken into account with more input data and modified classification criteria since DSM-5 classifies intellectual disability into four levels. [5]  Alongside its limitations, there are also strengths of the current study. In epidemiological studies of ASD, there is no uniform approach to case definition across published studies. For example, surveys of large national registrations or administrative databases usually result in a downward bias, and surveys that rely on parent report in a household survey often overestimate prevalence. [52]  One of the strengths of the GBD 2019 is that its estimates with known biases were adjusted or crosswalked for alternative definitions.
For instance, estimates of autism rather than of ASD, general population surveys without additional case finding, record report, and review of record notes, were adjusted (appendix Table  S2 , Table  S4 ). [18]  Additionally, consistent with previous epidemiological surveys of ASD,  [52]  there were very few studies from low SDI countries as classified by the World Bank 2020. GBD 2019 also utilized data inputs for ASD from 34 countries. However, GBD 2019 used an integrated modelling approach to estimate not only the epidemiological parameters for regions with available data, but also for the countries and territories, as well as regions in which sufficient data were not available. [18]  In summary, results of the present study show that raw ASD prevalence and related burden have increased over the last 30 years in high SDI countries, without concomitant large improvement of management strategies for the disorder (even though there has been progress over the last 30 years), which led to increased disability.
Results and discussion Virus-host network for known viruses (G c ). We developed a unipartite network with viruses as nodes and host species as edges for all species recognized as a host for viruses based on data presented in previous studies and databases, specifically, data shared by Olival et al. ,  4  Pandit et al. ,  3  and Johnson et al. 13  and GenBank. In the observed network (G c ), viruses were represented as nodes and a link (edge) was generated if two viruses had been detected in the same host species. The observed network (G c ) included 576 viruses as nodes and 35,838 edges (viruses linked because of shared hosts) representing 352 vertebrate species (Fig. 1 ). Exploration of network characteristics of known viruses revealed differences in host sharing among virus families. The distributions of centrality measures (Fig.
01) indicating more connections based on shared host species (Figs. S2-S5 ). Results based on distributions of centrality measures, as well as node level regression models, show similar directionality for Hantaviridae, Filoviridae, Flaviviridae, and Orthomyxoviridae families across multiple network topological metrics. Our findings provide further evidence for direct relationship between higher host plasticity and greater zoonotic potential  2, 4. The wildlife surveillance data consisted of tests for 99,375 animals, representing specimens from 861 species, mostly bats, rodents, primates, and other mammals (https://zenodo. org/ record/5899054) 1. To predict associations (linkages) between novel viruses with other viruses formed due to common host species, gradient boosting models were trained using network topological characteristics and families of viruses in the virus pairs to estimate: (1) whether virus pairs have a species host in common; and (2) the taxonomical order of shared hosts (Fig.
Methods Data collection. Virus-host data was collated from various sources. Major sources for the association databases included data shared by Olival et al  4. , Pandit et al. 3  , and Johnson et al. 13. In data provided by Olival et al (assessed September 2019), host-virus associations have been assigned a score, based on detection methods and tests that are specific and more reliable. We used associations that have been identified as the most reliable (stringent data) from Olival et al  4. In addition, a query in GenBank was run to parse out hosts reported for each GenBank submission for viruses presented in each of these three databases. Initially, for each virus name, taxonomic ID was identified using entrez. esearch function in biopython package. The taxonomic ID helped linked to the GenBank databases, identify the ICTV lineage and associated data in PubMed  20, 21.
NCBI TaxID closely follows the ICTV database, but some recent changes in ICTV might not always be reflected in NCBI, so we manually checked names to ensure matching. This included virus genus and family information along with a standard virus name. Host data were aggregated based on the taxonomic ID and associated standard name. Finally, for each virus, a search was completed in PubMed to compile the number of hits related to the virus and their vertebrate hosts using the search terms below. The number of PubMed hits (PMH1) were used as a proxy for sampling bias  3, 13   Along with the PubMed terms we also queried the nucleotide database on PubMed using the taxonomic ID to find the number of GenBank entries for these viruses (PMH2). A correlation analysis between the PMH1 and PMH2 of wellrecognized known viruses showed a high correlation with each other for us to safely use GenBank hits for novel viruses during the prediction stage of the model (Fig.