dataesr/scientific-paragraphs-categorization huggingface dataset:
External drift kriging is then widely used in air and soil quality modeling, in order to combine different kind of information to include secondary information in the model. Machine learning use algorithms and statistical methods to "learn" information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning allow for example to build a metamodel from a dataset of deterministic model outputs. The fundamental concepts of machine learning and its usages in spatially distributed data are given in Kanevskij et al  [28]. In order to construct the exposure maps from spatialized databases in the context of evaluating health risks, methods have been developed to process and harmonize the available data, with respect to their speci cities (missing values, limited number of observations, etc) in the same resolution and support.
A GIS-based modeling platform developed by INERIS for quantifying human exposure to chemical substances (PLAINE: environmental inequalities analysis platform  [29] ) aims to spatialize an environmental indicator related to human health using risk assessment methods and mapping environmental disparities at a ne resolution. The main aim of the PLAINE Project, developed in France, is to develop a platform of environmental and health data. This platform is developed for systematic collection, integration, and analysis of data on emission sources, environmental contamination, exposure to environmental hazards, and population and health. Ad-hoc methodologies are used to align the available data to the same pixels. Spatial analysis and statistical methods are employed to process (georeferencing, data controlling, pre-processing, re-formating) and assemble the databases for the purpose of the study, using R and QGIS.
By example, atmospheric concentration data were collected in France in the context of regulatory surveillance for two years (2010 and 2011). Estimation of concentrations over France by classical interpolation method could lead to a misrepresentation of the spatial distribution due to the limited number of observations. To address this issue, auxiliary variables in the context of external drift kriging  [30]  were employed. The best auxiliary variable to de ne linear drifts was found to be the one that includes the atmospheric emissions as well as the population and the altitude. Measurements of PAH topsoil concentrations are available through the French Soil Monitoring Network. Qualitative data on the polluted sites localization are integrated by processing distance-topolluted soil proxy. These, along with 14 variables about physicochemical soil properties were combined in a hybrid regression-kriging and tted using Random Forest  [31]  models, were shown to outperform the traditionally used linear regression.
Due to its hydrophobic nature, B[a]P is found in water in small concentrations; therefore, the exact measurement cannot always be reported. The observations under the detection limit rate is quite high, which requires careful handling. A complex multiple imputation method was developed in order to extract the maximum information from the available measurements without introducing too much bias in the results. This one permits to take advantage of the temporal aspect and correlations between substance of interest and other PAH substances. Spatial estimation of water concentrations was carried out by taking into account the multi-annual data and the network water distribution complexity using a bootstrap based expectation-maximization algorithm. The above methods permitted the construction of a representative spatial database in a 9 km 2 grid of reference on the whole France (550,000 km²) used to perform the integrated exposure assessment  [3].
However, in any case the computational effort associated with this modeling strategy is generally quite high and limits routine applications when a large number of chemicals need to be evaluated. A multimedia fate and exposure model called Modul'ERS  [41, 42]  developed by INERIS is used to estimate intakes from air inhalation and soil, tapwater, marketed food products, as well as local-produced fruits and vegetables ingestion. Local foodstuff concentrations are estimated using atmospheric deposition of particulate pollutants, air (for POP) and soil concentrations. As mechanistic and dynamic models for plants required many input data that can be di cult to de ne (lack of data, di culty for estimating the magnitude of variability and uncertainty of data and even anticipating the qualitative effect of variation of input data on results), contributions of air gaseous and soil concentrations to edible organs of plants are estimated from bioconcentration factors, which are speci c to the different categories of fruit and vegetables cultivated in domestic gardens and time average concentration during culture duration.
Therefore, the inputs of the model for media concentration estimates are georeferenced environmental databases (with a direct reuse of the treated data incorporated int the GIS for tapwater and marketed food products). In the model used, attention was focused on the quality of values used to de ne all inputs (exposure, environmental and chemical parameters). Analyses of available data were systematically conducted. For most of the parameters, all the data collected with their contextual information, as well as the selection criteria used are described in dedicated reports. Depending on the level of knowledge, the quantity and the relevance of the data available, the parameters are nally de ned with a point value, a range of values or a probabilistic distribution.
Study analysis ranges from 800 to 1780 cm -1. Multivariate analysis of data using PCA (principal component analysis) is carried out successively to preprocessing with the study of loadings. To explore the carbohydrate bands in more detail, peak deconvolution of the 930-1190 cm -1 zone is carried out with PeakFit v4. 02 software. During this Gaussian-Lorentzian area deconvolution, 6 peaks (980, 1018, 1049, 1078, 1108 and 1152 cm -1 ) are used to fit the band with half-height width considered as the same for each peak referenced. Minimal R 2 accepted to fit is 0. 99. After peak deconvolution, area percentage contribution is calculated for each peak of the whole massif band.
Quantitative PCR (Q-PCR) is also carried out in two steps, using a StepOne-plus Real-Time thermocycler (Applied Biosystems) with SYBER-Green Q-PCR Master Mix (Promega, Madisson, USA). Seven target genes (five involved in polysaccharides biosynthesis and two in carotenoid biosynthesis) are selected in accordance with previous work  (Couso et al. , 2012; Lagarde et al. , 2016; Wang et al. , 2016)  and primer sequences were designed with Primer3-Plus software (http://www. bioinformatics. nl/cgi-bin/primer3plus/primer3plus. cgi). Choice of genes for the sugar biosynthetic pathway was carried out according to those known in the literature and the sequence of which is available in the databases (https://genome. jgi. doe. gov/portal/). Efficiency and melting curves for each primer pair (Table  1 ) were produced on cascade dilutions of a cDNA mixing.
The 1:8 dilution and the primers with optimal efficiency range of 85%-111% were selected for the second phase of Q-PCR. The Ct (threshold cycle) comparison method  (Livak and Schmittgen, 2001)  was applied to calculate the gene expression level. Housekeeping genes, rps2 and rps14, presenting the best stability among tested conditions, were selected with Best Keeper software  (Pfaffl et al. , 2004)  and used for normalization of the gene expression studied. Gene expression level (EL) was calculated from Ct of each sample: (EL= ((ETG) ΔCt TG ) / (EHG) ΔCt HG ), (TG: Target Gene; HG: Housekeeping Gene; ΔCt= CtControl-CtSample). Amplification program consisted of 15 s at 95 °C followed by 35 cycles of one minute to decrease to 60 °C.
Statistical analysis All statistical analysis were carried out using Graph-Pad Instant software (GraphPadSoftware Inc. , La Jolla, USA). Continuous variables, such as body weight, body weight gain, food and water consumption, hematology, clinical chemistry, and organ weight, were summarized using standard measures of central tendency and dispersion, mean and standard deviation (ST. DEV. ), and were reported by sex and dosage. One-way analysis of variance (ANOVA) was carried out to test differences in continuous variables. Normality assumption was tested using Kolmogorov-Smirnov's test. If non-normality, comparison was performed with Kruskal Wallis test. In case of significant differences, multiple comparisons were performed using Tukey-Kramer/Dunn's Multiple Comparisons Tests. Differences were considered significant from P < 0.
DATA The study was based on analyses of secondary data using descriptive and inferential statistical methods. We analyzed data on malaria cases and the water level of 35 municipalities in the state of Amazonas. The number of analyzed municipalities was associated with the quality of data on local hydrometric stations. Data covered different periods due to the integrality of the historical series of hydrometric stations. REV BRAS EPIDEMIOL 2020; 23: E200018.
Water level Hydrological data from monitoring stations were gathered from the database of the National Water Agency (Agência Nacional de Águas -ANA) and the Observation Service for the geodynamical, hydrological and biogeochemical control of erosion/ alteration and material transport in the Amazon, Orinoco and Congo basins (ORE-HYBAM), available at http://www. ore-hybam. org/. A total of 35 hydrometric stations from 6 distinct basins of the Negro, Japurá, Solimões/Amazonas, Juruá, Purus, and Madeira rivers were analyzed in the 2000-2010 period, on a monthly scale. Years prior to and after the assessed period lacked data of over three months, making it impossible to use the series.
Malaria cases The survey on malaria cases was conducted by the processing of raw data, compressed in DBF files, made available in the database Information System for Epidemiological Surveillance of Malaria (Sistema de Informação de Vigilância Epidemiológica da Malária -SIVEP-MALARIA). We analyzed 35 municipalities (Figure  1 ) for the 2003-2014 period. Since SIVEP does not have records prior to 2003, the data assessed were from that year onwards.
ACKNOWLEDGMENTS We thank the support received from the  Institut de Recherche pour le Développement (IRD) ,  Maison de Télédétéction (Montpellier, France) , the  Laboratório de Potamologia da Amazônia (LAPA) , and the  Graduate Program in Climate and Environment  of the  Instituto Nacional de Pesquisas da Amazônia (PPG -CLIAMB/INPA ).
, 2013). At interannual timescales, SST has been linked to depth-integrated heat content in the Barents Sea  (Chepurin & Carton, 2012; Lique & Steele, 2013). In the present analysis, we aim to calculate BSW properties by combining satellite data parameters. Our first objective is to reconstruct the time series of depth-integrated heat and freshwater contents between 2003 and 2013 based on a statistical method applied to satellite data. Second, we examine the interannual variability in BSW and its drivers. We chose the Barents Sea as a test site for this study due to the relatively large collection 10. 1029/2019JC015449 Journal of Geophysical Research: Oceans of historic hydrographic profiles  (Oziel et al. , 2016). This study further focuses on the formation region of BSW, a precursor to AIW.
Data We used satellite-derived SSH, OBP, and SST to estimate steric height and heat and freshwater contents, which was then verified using in situ observations. In this section we describe the data sets and detail the methods used. Data on SSH were provided by the Centre for Polar Observation and Modelling (CPOM)  (Armitage et al. , 2016). To obtain SSH everywhere in the Barents Sea, including under sea ice, a processing technique has been applied to satellite altimeter data that takes into account the difference in character of satellite echo-return signals from specular surfaces (ocean between leads) and nonspecular surfaces (snow and sea ice)  (Giles et al. , 2012; Laxon, 1994). The product has previously been used to examine Arctic Ocean circulation  (Armitage et al. , 2017; Regan et al.
57 K  (Donlon et al. , 2012) , but cannot resolve mesoscale variability (with a characteristic scale of only a few kilometers) in the Barents Sea, although the resolution does show fronts in the region  (Barton et al. , 2018). Temperature and salinity fields from the EN4 data set were used for evaluating the satellite data (EN4. 2. 0, www. metoffice. gov. uk/hadobs/en4). EN4 comprises in situ ship CTD profile data and Argo float data optimally interpolated on a 1°, monthly z grid with 42 levels  (Gouretski & Reseghetti, 2010). Data are available from January 1980 to December 2016. Within the Barents Sea (10°E to 65°E and 68°N to 80°N) over this time frame, every 3 month period has more than 117 profiles with a total of 33,323 profiles in this region during our study period. These profiles are biased toward September, October, and November when there are usually more than 400 profiles, and there is a spatial bias in favor of the southwestern Barents Sea.
, 2015; GEBCO_2014 Grid, Version 20150318, www. gebco. net). In the Barents Sea, it corresponds to a grid of 0. 2 km in longitude and 0. 9 km in latitude. In the following analysis, we focused on the period when all the observational data sets are available, January 2003 to December 2013. EN4, GRACE, OSTIA, and ECMWF data were linearly interpolated on the same grid as the CPOM SSH. By default, GRACE data are masked around land. Before interpolation onto the CPOM SSH grid, we extrapolated GRACE data spatially toward the coastal areas by one grid cell using weighted averaging of the nearest neighbors. This is reasonable given that GRACE data have a 500 km filter.
Methods Steric height can be calculated using three methods: (i) from SSH and eustatic height  (Armitage et al. , 2016) , (ii) from halosteric and thermosteric heights  (Gill & Niiler, 1973; Volkov et al. , 2013) , and (iii) through the integration of in situ density  (Roquet et al. , 2015). Here, we regarded steric height derived from integrating EN4 data as the ground-truth data for validating the satellite-inferred estimates. Thermosteric and halosteric heights can also be derived from temperature and salinity profiles and are statistically proportional to heat and freshwater contents  (Steele & Ermold, 2007). We converted heat and freshwater contents to halosteric and thermosteric heights, giving us parameters with the same units as steric height (in m). We used the following methods, largely based on previous work for estimating the various components of SSH.
Freshwater content, F, used by  Lind et al. (2018)  was defined as FðtÞ ¼ Z h1 h2 S r -Sðz; tÞ S r dz (6) where S(z, t) is salinity as a function of depth and time. As we estimated steric height relative to T = 0°C  Journal of Geophysical Research: Oceans and S = 35, freshwater content is also estimated relative to reference salinity S r where S r = 35. Similar to thermosteric height, in this paper, we derived halosteric height from freshwater content: η S ðtÞ ¼ b β -FðtÞS r þ Z h1 h2 S r -SðzÞdz (7) where b β is the coefficient of haline contraction averaged over time and depth. We applied these equations to the data, allowing us to compare satellite steric height calculated using Equation  1 , with in situ steric height calculated from EN4 data using Equation  2. Thus, to avoid confusion, hereafter, quantities will be prefixed by satellite or in situ respective to the source of data they were derived from.
Note that the satellite steric height is noisier than the in situ steric height due to inherent noise in the two satellite products used to calculate it. To account for this and to focus on timescales that are likely to be resolved in the different data sets, we smoothed the data with a 3 month running mean. Consequently, our methods can account for seasonal variability but will not resolve higher-frequency variability. To account for the spatial variability in the Barents Sea, we calculated properties at each individual grid point of the interpolated data sets. The in situ steric height was calculated from the surface downwards to each depth. We regressed in situ steric height at each depth against satellite steric height. We identified the optimum integration depth (OID) from the maximum regression value at each grid point (Figure  2 ).
10. 1029/2019JC015449 weighting both the in situ and satellite data for the northern Barents Sea in winter. However, since this study is focused on the BSW formation zone in the Central Basin, we have selected a biseasonal approach that is robust for our region of interest. We estimated the uncertainty of the satellite OID steric height to determine the quality of the measurement and how far they may deviate from the true value. The uncertainty of the satellite OID steric height was estimated from the uncertainties of the component data sets, OBP and SSH, using quadrature combination of uncertainty. This was applied to Equation 1 as Δ η st ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi mðΔ 2 η H þ Δ 2 η m Þ q where Δ is the respective uncertainty of η st (steric height), η H (SSH), and η m (eustatic height).
This implies that our analysis for satellite halosteric height (detailed in the next paragraph) could be used to expand and improve the satellite SSS record over a longer period, starting in 2003. Therefore, we assume that in situ SSS is best represented by the first depth bin (0 to 10 m depth) of the EN4 salinity field. We acknowledge that a better in situ SSS-to-OID freshwater content fit is expected as we are making comparisons within one data set (EN4), in contrast to the satellite SST to OID heat content model for which data from independent sources were considered. Accordingly, the in situ SSS-to-OID freshwater content model at our illustrative location shows that SSS is representative of freshwater content in the upper water column (Figure  6a ). There is a smaller difference between the summer and winter seasons and less dependence on the month considered than the satellite SST to OID heat content model.
Journal of Geophysical Research: Oceans Using satellite OID steric height and satellite OID thermosteric height estimated from heat content with Equation  5 , we use Equation 3 to estimate the satellite OID halosteric height (the reconstruction for our example location is shown in Figure  4c ). The ability of the satellite model to retrieve an accurate OID halosteric height time series is significant, with regression values with the in situ OID halosteric height between 0. 4 and 0. 55 in the Barents Sea (significant at the 99% level) (Figure  7 ). Although the regression is not strong it shows that satellites can nevertheless provide information about OID halosteric height variability. There are only a few EN4 profiles in the Kara Sea making the data sets here more representative of the climatology in this region.
When satellite OID heat content is converted to satellite OID thermosteric height, thermosteric height has an uncertainty of 1. 1 cm. Using quadrature uncertainty combination of the satellite OID steric height and thermosteric height, the uncertainty of the estimated satellite OID halosteric height is 1. 5 cm and the uncertainty of satellite OID freshwater content is 0. 6 m. For this study we note that the gridded EN4 data set does capture the variations of the water mass properties but that the irregular sampling, particularly during winter and spring, means that temporal inaccuracies are inevitable. Individual T-S profiles used in producing the gridded EN4 product can also be biased by mesoscale features that are not resolved in the satellite data. Figure  7  suggests no significant regression between satellite and in situ OID halosteric heights in the Norwegian Sea.
The steric height and halosteric height are both limited by the 500 km spatial feature resolution of GRACE, and temporal feature resolution is seasonal. Halosteric height will be more limited by sea ice than steric height because of its reliance on SST. The satellite data have lower accuracy than EN4 and do not offer information on vertical structure. The satellite data give information on the OID and assume a seasonally dependent OID depth for each location. These data should be used as complementary to EN4 or other profile data sets.
Conclusion Heat content variability in the Barents Sea is a useful predictor for future sea ice extent  (Årthun & Eldevik, 2016; Lind et al. , 2018; Onarheim et al. , 2015; Schlichtholz, 2019)  and is, therefore, an important quantity to accurately monitor to help with understanding the sea ice-heat content coupling. Our analysis shows the potential for combining OBP, SSH, and SST satellite data to determine monthly time series of satellite upper ocean OID steric height, heat and freshwater contents for major parts of the southern Barents Sea. This is important for the local ecosystem  (Eriksen et al. , 2011; Oziel et al. , 2017) , and the southern Barents Sea is a region where SST is correlated with anomalous winter surface air temperature across Europe and Asia  (Blackport et al. , 2019; Hoshi et al.
Data Availability Statement Data supporting these conclusions can be obtained from web addresses in section 2. 1. GRACE ocean data were processed by Don P. Chambers, supported by the NASA MEaSUREs Program, and are available online (http://grace. jpl. nasa. gov). Arctic dynamic topography data were provided by the Centre for Polar Observation and Modelling, University College London (www. cpom. ucl. ac. uk/dynamic_topography)  (Armitage et al. , 2016).
| Data collection Researchers are increasingly motivated to share sampling locations on online archives, resulting in continuously growing databases. For example, the European Vegetation Archive (EVA) counted 1,529,550 vegetation plots of species co-occurrence data most of them with geographical coordinates  (Chytry et al. , 2016 , see also sPlot:  Bruelheide, Dengler, Jiménez-Alfaro, & Purschke, 2018a , Bruelheide et al. , 2018b). In addition, the Global Biodiversity Information Facility (GBIF) currently contains 1,074,675,056 georeferenced occurrence records of microorganisms, plants, and animals. Exploring such databases for the species of interest in combination with information obtained through regional nature conservation organizations and publications can improve the detail of the spatial extent of the respective species (e.
g. ,  Cardador, Carrete, Gallardo, & Tella, 2016; Diniz-Filho et al. , 2016)   (Figure 2. 1). In parallel, researchers should continue to share their species survey data on publicly available repositories, while increasing computational power and data accessibility might increase the number of species for which mechanistic SDM can be parameterized. For instance, in birds, a high number of telemetry datasets were made available in public repositories such as Movebank (https://www. moveb ank. org) that may help parameterizing dispersal in trait-based SDM. The integration of ecology and physiology required for trait-based approaches will also certainly be favored by the routine integration of metabolic data into life-history database such as Pantheria  (Jones et al. , 2009). A struggling point complicating SDM for many species is the lack of sufficient absence and occurrence data, which is problematic because species detectability often varies in space and time, resulting in poor model performance and biased SDM outcomes  (Guillera-Arroita, 2017; Merow, Wilson, & Jetz, 2017; Morán-Ordóñez et al.