dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank the many researchers who collected the data used in this publication and the research sites and funding agencies that support long-term research. We acknowledge support from  LTER Metacommunities Synthesis Group  (NSF DEB# 1545288 ), administered by the  Long-Term Ecological Research Network Office (LNO)  located at the  National Center for Ecological Analysis and Synthesis (NCEAS), University of California, Santa Barbara. Research was also facilitated by the  National Ecological Observatory Network (NEON) , sponsored by the  National Science Foundation  and administered by  Battelle Memorial Institute. Logistical  and data management support was provided by the Environmental Data Initiative (EDI). JDT is supported by a  Rutherford Discovery Fellowship  administered by the  Royal Society Te Apārangi  ( RDF-18-UOC-007 ) and  Bioprotection Aotearoa  and  Te Pūnaha Matatini , both  Centres of Research.
3. Data collection The experiment was conducted from January to December 2020. March 2020 is excluded from the results because of the COVID-19 sanitary crisis, which prevented data collection in Costa Rica. The study site was visited monthly and each Pocillopora fragment was photographed with an underwater Nikon COOLPIX W300 camera, using a calliper as a scale (Fig. 2b ). Photographs were later analysed using ImageJ software, which allows for a 0. 001 cm precision, and height (cm), width (cm) and area (cm 2 ) of each fragment was determined. This allowed us to estimate a growth rate in terms of linear extension (cm year -1 ) and tissue area (cm 2 yr -1 ). Linear extension was calculated by measuring the vertical length between the two longest coral branches, while the area of the coral was estimated by outlining the contour of the coral fragment, and subsequently calculating the average.
Mortality was visually determined; a fragment was considered dead if it had no living tissue left and/or was covered by other organisms such as algae, barnacles or ascidians. If the fragment was partially dead, only the part with living tissue was measured. The number of dead fragments was established and used to calculate fragment survival rates. Seawater temperature in the restoration area was recorded using HOBO® data loggers, which were set to record data every 30 min.
Acknowledgements The present investigation would not have been possible without the support of  Centro de Investigación en Ciencias del Mar y Limnología (CIMAR)  of the  University of Costa Rica  and students who helped during fieldwork and data collection for this project. We would also like to thank  José Francisco Cascante , who built the structures used in this experiment. We especially thank  Península Papagayo  for their economic and logistical support. Fieldwork would not have been possible without the help from  Carlos Marenco  and the staff of  Marina Papagayo , to whom we are really thankful.
Materials and Methods L-VOD AGB. Satellite-based L-VOD data at 25-× 25-km resolution covering the period from 2010 to 2019 from the SMOS (SMOS-IC, version 2) satellite is used for the analysis. The method used here to compute AGB from L-VOD in the same as the method used in refs. 8, 46, and 47; that is, we use four different static AGB benchmark maps to calibrate the relationship between L-VOD and AGB for the entire tropical region, South America, and Africa separately. The calibration process for only Southeast Asia is not used because of the limited SMOS observations in this region. The AGB benchmark maps include three pantropical maps (i. e. , refs. [48] [49] [50]  and one map covering only Africa  (51). After the calibration, 10 maps of L-VOD AGB can be obtained, and the median of these 10 maps of monthly L-VOD AGB covering the period of 2010 through 2018 is used in this study.
The responses of AGB during drought are calculated as the averages of AGB stocks during the 2015/16 drought period minus the averages of AGB stocks for the same months, but during the years 2014 and 2015 (ΔAGB 14/15!15/16 ): ΔAGB 14=15!15=16 ¼ AGB 15=16 À AGB 14=15 : We also calculate the average of AGB for the same months, but during the years 2016/17, 2017  /18, and   (34, 56). It operates by constructing a multitude of decision trees from a training dataset. Each tree is trained on a random subset of data, and the results of all the decision trees are then combined to give a final prediction. Model performances were assessed with the mean absolute error (MAE) and the R 2 value. The selected model has the smallest MAE and the highest R 2. The spatial patterns of the RF-predicted values are compared with the observed values (SI Appendix, Fig.
The predictor variables of drought characteristics used are predrought ACWD (ACWD pre ), postdrought ACWD (ACWD post ), drought severity, drought duration, soil moisture (SM), and VPD anomalies during drought and after the drought. Soil-moisture data are from the SMOS-IC satellite. VPD is calculated by using reanalysis climate products from the Modern-Era Retrospective Analysis for Research and Applications, The predictor variables of background climate used are the average of temperature, precipitation, and radiation during the drought period and precipitation seasonality (SD of 12-mo precipitation). The climatological monthly temperature, precipitation, and radiation are from the WorldClim database (https://www. worldclim. org). The predictor variables of soil property are soil clay content (%) from SoilGrids (https://soilgrids.
Spatially resolved external data, namely Cd concentrations in soil, agricultural areas, phosphate fertilizer application, traffic density and point source Cd release were collected for the respective statistical territorial unit (NUTS). There were no distinct geographic patterns observed in Cd levels in urine, although the data revealed some differences between the specific study sites. The levels of exposure were otherwise similar between two time periods within the last decade  (DEMOCOPHES -2011 -2012 vs. HBM4EU Aligned Studies, 2014 -2020). The age-dependent alert values for Cd in urine were exceeded by 16% of the study participants. Exceedances in the different studies and locations ranged from 1. 4% up to 42%. The studies with largest extent of exceedance were from France and Poland. Association analysis with individual food consumption data available from participants' questionnaires showed an important contribution of vegetarian diet to the overall exposure, with 35% higher levels in vegetarians as.
Study design and sampling frame The study is part of the European Human Biomonitoring Initiative (HBM4EU) conducted in 21 European countries, the so-called HBM4EU Aligned Studies. It builds on existing HBM capacity in Europe by aligning national or regional HBM studies targeting the general population  (Gilles et al. , 2021). The studies were aligned with respect to the sampling period  (2014) (2015) (2016) (2017) (2018) (2019) (2020) (2021) , age groups (children, teenagers and young adults), sampling size (240-300 per study) with a 1:1 male to female ratio, biomarkers of interest, and questionnaire data available per age group. Residents in hotspots, patient groups, or specific occupational groups were excluded. Studies of all 4 geographical regions of Europe according to the United Nations geo-scheme (North, West, South, East) were included, with at least two countries per region.
Questionnaire and ancillary data Accompanying data were available from the questionnaires and spatially resolved data extracted from the available European databases. Since the HBM4EU Aligned Studies aligned both new, ongoing and recently conducted studies, a post-harmonization approach was applied to harmonize the collected questionnaire data. Questionnaire data included personal information (sex, age, educational level, country of birth), socio-demographic information on the participant (socio-economic status -income, current occupation status, occupational sector and occupational activities), information on residential environment (country of residence, NUTS level 1, 2, and 3, type of residence/degree of urbanization, residential history, density of traffic in the residential area, farmlands, orchards or vineyards in vicinity, frequency of dusting and vacuuming), information on dietary habits (vegetarian diet, consumption of local food, seafood, fish, meat, poultry, vegetable and fruit, offal and cereals, type and source of drinking water), lifestyle information (smoking status, number of cigarettes smoked per day, passive smoking exposure), personal care and health (height, weight, body mass index, chronical illness, physical activity, and pregnancy status and parity if the subject was female).
Educational level was used as a surrogate for socio-economic status. The classification was based on the International Standard Classification of Education (ISCED)  (Gilles et al. , 2021). Additional supporting data for the statistical analyses were obtained from various data sources available at European level. Two sources with different spatial resolution were used for Cd distribution in soil. Distribution of Cd in topsoil mapped based on the FOREGS (Forum of European Geological Surveys) geochemical database with 5 km resolution  (Lado et al. , 2008) , and a map of Cd concentration in the topsoil of the European Union produced based on the LUCAS (Land Use/Land Cover Area Frame Survey) data and available in 1 km resolution  (Tóth et al. , 2013 (Tóth et al. , , 2016)). Moreover, the following ancillary data were extracted from the statistical office of the European Union (Eurostat): percentage of agricultural areas and cropland, respectively; annual application of phosphate fertiliser reported as consumption by countries or estimated in tonnes; and the density of motorways and other road network in km per km 2.
The data were extracted for the year(s) most closely corresponding to the year of conduct of the respective study. Annual releases of Cd to air and water from industrial facilities located within the individual NUTS regions as reported in the European Pollutant Release and Transfer Register (E-PRTR) (https://prtr. ec. europa. eu/) in the period 2008-2015 were also considered. All data were extracted and assigned to the HBM data at NUTS unit level at the finest resolution possible, depending on the spatial resolution of the source data  (EUROSTAT, 2018). Visualization of the acquired external data is provided in the Supplemental material (Fig. S1 ).
Statistical analysis In case of Cd levels below LOD/LOQ, random values were imputed between 0 and LOD, between 0 and LOQ, or between LOD and LOQ (depending on the values reported by the data provider) using a truncated lognormal distribution. Urinary Cd concentrations were standardized by creatinine as estimator of urine density, as it was available for all studies. The descriptive statistics (N, geometric mean (GM) and 95% confidence intervals (CIs) and percentiles P05, P10, P25, P50, P75, P90, P95) were calculated using unadjusted data for each country. The levels are reported per volume (μg/L) and standardized for creatinine (μg/g creatinine). As the studies differed in the type of urine sample (Table  1 ), creatinine-standardized Cd concentrations were used in data interpretation. European exposure values were calculated using survey procedures to take into account the complex survey design when calculating variance estimates  (Park and Lee, 2004) , for the pooled population, and stratified by sex, educational level, smoking, degree of urbanization and geographic region (North, South, East, West).
European exposure values For derivation of European exposure values, we used survey design data analysis with country as a primary sampling unit (PSU). Because of the fact that the biomarker data in the French study was not quality assured by HBM4EU, this study was excluded from the calculation of European exposure values. The calculated levels are expressed per volume (μg/L) as well as per creatinine (μg/g creatinine) in order to make comparison with exposure values from other studies worldwide possible.
Most covered was data for vegetarian diet, available in studies from Iceland, Czech Republic, Croatia, Portugal, France and Germany. Among these, very few participants reported to be vegetarians (3%), with the highest percentage in the German (7%), and the lowest in the Portuguese (0. 7%) and French (1%). Organ meat consumption was available in studies from Czech Republic, Poland, Croatia, France, Luxembourg and Germany. Along with the dietary data, drinking water data was checked against Cd levels in urine, namely source (public, private well) and type of drinking water consumed by the subject (bottled, tap, ground or other type of drinking water). Marginal significance was found only in the French data, where the participants drinking water from 'other' sources had higher urinary Cd levels than those drinking bottled or tap water (Table  S2 ).
906). Traffic density was also not found to be significantly associated with Cd in urine in the pooled model (p = 0. 896), and neither was the degree of urbanization (p = 0. 869) (data not shown). As part of sensitivity analysis, the model was applied on the subset of database with complete data on vegetarian diet (n = 1631) (not shown). The coefficients stayed exactly the same as shown in the Model 2 (Table  6 ). An additional model was checked applying the WHO creatinine criteria for urine levels exclusion based on creatinine levels (not shown). This model showed similar coefficients as shown in Model 2,  unless for vegetarian diet where the coefficient was even slightly higher (1. 40, n = 1545). We also stratified the model based on the type of the urine sample -spot random sample and spot morning sample, while the model with daily urine sample type was not considered as it was only available for one country.
Discussion The paper deals with the assessment of current Cd exposure in the European population of adults and it is based on a study population which has been harmonized in terms of basic population characteristics: age (20-39 years), sex (female to male ratio 1:1), sample size (240-300), sampling period (2014-2021), exclusion of hotspots and patients, biomarkers of exposure (Cd concentration in urine), and partially also questionnaire data (socio-demographic characteristics, life style, health status, residential environment and diet). The harmonization process has been established within the HBM4EU project, which also coordinated the quality control procedure for measurement of Cd in urine to ensure comparability of analytical (biomarker) data. To ensure geographical comparability, two studies per region (North, East, South, West) were included.
General characteristics of the study population The study population of adults for assessment of Cd exposure (n = 2510) included 53. 8% women and 46. 2% men 20-39 years of age. According to the residential area 64. 9% live in cities, 17. 45% in towns/ suburbs and only 17. 65% in rural areas. The percentages are only slightly different from the percentages for the total HBM4EU Aligned Studies adult population (n = 3522) for which the characteristics were concluded to approach the characteristics of the general European population based on age matched EUROSTAT EU-28, 2017 data  (Gilles et al. , 2022). However, participants with no or low level of education (ISCED 0-2) were largely underrepresented (5%). The percentage of smokers in the study population was 18%, which is very similar to the percentage of every day smokers in the general European population in 2014 (19.
20 μg/g crt and for DEMOCOPHES 0. 18 μg/g crt  (Berglund et al. , 2015). In both surveys, the highest levels were observed for Poland and the lowest for Denmark. The DEMOCOPHES Polish study reported a GM of 0. 38 μg/g crt in all mothers and 0. 36 μg/g crt in non-smoking mothers  (Berglund et al. , 2015; den Hond et al. , 2015). Practically the same GM levels were observed in the HBM4EU Polish dataset (0. 38 μg/g crt, both all and non-smoking women) (Table  S4 ). On the lower exposure end, Denmark reported a GM of 0. 12 μg/g crt for all mothers in DEMOCOPHES and 0. 11 for non-smoking ones, while in HBM4EU the GM for all female participants was 0. 14 μg/g crt and 0. 13 μg/g crt for non-smoking ones  (Berglund et al. , 2015; den Hond et al. , 2015)  (Table  S4 ). Similar to the Polish and Danish studies, also Luxembourg shows stagnating levels in comparison to the DEMOCOPHES assessment.
, 2013; UPB, 2022). A similar situation was observed in a recent comparison of urinary Cd data for children in the German Environmental Survey (GerES), where overall, participants in GerES V (2014-2017) had about 15% lower Cd concentrations than GerES  II (1990-92)  children and adolescents, but not lower then GerES IV participants  (2003) (2004) (2005) (2006) )  (Vogel et al. , 2021). France was not part of the harmonized DEMOCOPHES survey, but as apparent from the literature data, the levels in this country seem to stagnate as well. Nisse et al. reported a GM of 0. 39 μg/L Cd in urine of participants from Northern France for the period  2008-2010 (n = 1992) , which is similar to the French study included in the aligned survey (0. 37 μg/L, Table  3 )  (Nisse et al. , 2017). The above listed comparisons show somehow varying trends between the countries, but there is firm evidence indicating that overall Cd exposure levels in the general population are currently not decreasing markedly.
In addition to the diet-related intake, our results demonstrated traffic and industrial releases as significant contributors to Cd exposure in the general population. Traffic intensity close to home has been shown as a statistically significant determinant for blood Cd in children from six European countries, whose Cd measurements had also undergone strict QA/QC procedures and were performed centrally and varied little between the countries  (Hrubá et al. , 2012). The authors explanation follows the fact that, although in small amounts, vehicle tires contain zinc contaminated by Cd. In the present study, traffic density in the participant's residential area was available through the post-harmonized questionnaire data with three categories (no traffic, light traffic, intense traffic) and through the Eurostat database (km roads/km 2 ).
Statistically significant association with Cd concentrations in urine was observed for the latter, which demonstrates that NUTS2 level external information is superior to the data from questionnaires, reported by participants. Industrial point-source release of Cd to air and water is an additional potential source of Cd exposure, which is available from the E-PRTR database, and has been used as an external dataset for interpretation of HBM data beforein the DEMOCOPHES study  (Smolders et al. , 2015). There, the association between annual releases on a NUTS2 levels and mother's or child's urine Cd concentrations has not been observed. The association has been re-analyzed in the present study together with other potential sources of exposure, and it was revealed as significant (Fig. 4 , Table  6 ).
As a consequence, the statistical models were not entirely comparable between each other (each was built on a different dataset) and it was not possible to include all available potential sources of exposure into a single model. Another issue is related to urinary Cd concentrations and standardization of the levels for urine dilution and the risk of underestimating the exposure due to creatinine over-compensation  (Hoet et al. , 2016). We could overcome this issue by using standardization based on specific gravity, which appears to be a more reliable alternative in the context of environmental exposures, without the risk of over-adjustment and with fewer uncertainties associated with its use  (Hoet et al. , 2016; Stajnko et al. , 2017). Unfortunately, measurements of specific gravity could not be used in the present study, as they were only available for one study.
Funding The  HBM4EU  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 733032. Co-funding for the  HBM4EU  Aligned Studies has been provided by the national programs:  Santé Publique France  and the  French ministries of Health  and the  Environment (ESTEBAN, France) ;  MEYS  (No. LM2018121 ), and  Cetocoen Plus project  ( CZ. 02. 1. 01/0. 0/0. 0/15_003/0000469 ) (CELSPAC: YA, Czech Republic ); the  Ministry of Science and Higher Education of Poland  (contract no. 3764/H2020/ 2017/2 ) (POALES, Poland);  Public Health Fund (Diet_HBM, Iceland) ;  Croatian Institute of Public Health  (HBM survey in Croatia);  National Institute of Health Dr Ricardo Jorge (INSEF_ExpoQuim, Portugal) ;  German Ministry  for the  Environment, Nature Conservation ,  Nuclear Safety and Consumer Protection (BMUV)  ( ESB, Germany) ;  Luxembourg Institute of Health (LIH) , the  Laboratoire national de santé  (human biomonitoring part), the  Ministry of Higher Education and Research of Luxembourg  and the  Ministry of Health of Luxembourg (Oriscav-Lux2, Luxembourg) ;  Candy Foundation  (Nos.
2017-224  and  2020-344 ),  Absalon Foundation  (No. F-23653-01 ), The  Danish Environmental Protection Agency (Miljøstyrelsen :  MST-621-00012  Center on Endocrine Disrupters), The  Research council of Capital Region of Denmark  (No. E-22717-11 ),  Research council of Rigshospitalet  (Nos. E-22717-12 ,  E-22717-07 ,  E-22717-08 ),  Aase og Ejnar Danielsens Fond  (No. 10-001874 ),  International Research and Research Training Centre for Male Reproduction and Child Health  (EDMaRC, No. 1500321/1604357 ) (CPHMINIPUB (parents) and  DYMS, Denmark). J. Kl. and L. A. thank the CETOCOEN  EXCELLENCE  project No. CZ. 02. 1. 01/0. 0/0. 0/17_043/0009632  financed by  MEYS  for supportive background, and supported from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No.
Governing Equations The mixing processes by dispersion in a media (also porous) denoted by Ω characterized by a HDT + ∇. ( → v C) = ∇. ( = D∇C) + S C ( 1 ) where C is the diffused concentration, t is the time, ∇. and ∇ are respectively the divergence and the gradient operators and S C is the term source or sink term. It should be noted that the velocity field → v denotes the velocity of the fluid flow carrying the concentration C through the studied porous medium. This velocity was calculated beforehand by the numerical solution of Darcy's law by the LBM method implemented in the computer code of HYSFLO-LBM  [20]. As specified in the introductory section, here, we are interested in the isotropic HDT, therefore in the 2D modeling, the tensor = D will be of the form: = D = D ij 1 ≤ i ≤ 2 1 ≤ j ≤ 2 = Dδ ij with δ ij = 1 i f i = j 0 i f i = j (2) To maintain isotropy, the diffusion coefficient D must be taken in the directions of the flow direction, by considering the following expression of the D : D(x, y) = D m + K T (x, y) → v (3) where D m is the molecular diffusion, K T is the transversal diffusivity and is the → v modulus of the flow velocity.
However, the LBM method solves the PDE (5) of unknown the DF f. It is then necessary to transform the boundary conditions relating to C variables to new ones which will be imposed on the DF f. In  [28, 29]  one can find various transformations which make it possible to impose the various types of boundary conditions (Dirichlet, Neuman, Robin, etc. ). To finish this subsection, we give the LBM algorithm which was implemented to solve the PDE (4) to build the computation code HYDRODISP-LBM (Algorithm 1). ( f k ) 0≤k≤8 ) set boundary condition compute C = ∑ 8 k=0 f k test C -C 0 / C 0 < tol exit set C 0 ← C go to the next time step One can remark that the velocity field → v is a datum for the DP algorithm. This velocity field can come from any numerical model solving the Darcy's equation in the concerned study area.