dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , phenylalanine and glycine) track the δ 15 N values at the base of the food web, while trophic AA (Tr-AA; e. g. , glutamic acid), being enriched in 15 N with each trophic level, provide information about a consumer's TP  (Popp et al. , 2007). Overall, the combination of muscle stable isotope ratios and AA-CSIA is a powerful tool to investigate both Hg methylation and biomagnification along food webs. The specific objectives of our study were to i) investigate long-term trends of Hg concentrations in tuna from the New Caledonia-Fiji sub-region in the southwestern Pacific Ocean, and to ii) identify the main drivers of inter-annual variability of tuna Hg concentrations among physiological, ecological, biogeochemical and physical parameters. We analysed a unique long-term, continuous and large Hg dataset (n=590) in three commercial tropical tuna species (yellowfin, Thunnus albacares; bigeye, T.
Environmental variables: Seven oceanographic variables shown to influence variability of Hg concentrations in tropical tuna  (Houssard et al. , 2019)  were used in this study to explore the physical drivers of Hg concentrations at the surface and at depth. Surface variables included monthly mean sea surface temperature (SST in °C) from the National Oceanic and Atmospheric Administration (NOAA, https://www. ncdc. noaa. gov/oisst; (Reynolds et al. , 2002)   1997). Variables at depth or sub-surface included the mixed layer depth (MLD) and depths of the 12°C and 20°C isotherms (Diso12 and Diso20, m), all obtained from the monthly global ARMOR3D L4 dataset  (Guinehut et al. , 2012). The Oceanic Niño Index (ONI), used to monitor the El Niño-Southern Oscillation (ENSO), was also collected over the study period.
, 2012) , all oceanographic variables were averaged over a six-month period preceding individual capture date to explain both Hg and nitrogen isotope data. Detritus) ecosystem model  (Somes and Oschlies, 2015). The processes in the model that fractionate the nitrogen isotopes (i. e. preferentially incorporate 14 N into the product) are phytoplankton NO3 assimilation (6 ‰), zooplankton excretion (4 ‰), N2 fixation (1 ‰), water column denitrification (20 ‰) and benthic denitrification (6 ‰), in which the respective fractionation factor yields the δ 15 N difference between substrate and product  (Somes et al. , 2010). The baseline δ 15 N values reproduce the major features in a global seafloor δ 15 N database  (Tesdal et al. , 2013). Complementary Hg data (n=132, only skipjack tuna) were analysed at the IRD laboratory in Noumea (New Caledonia) by hot plate acidic digestion (HNO3-H2O2) followed by Cold Vapor Atomic Fluorescence Spectroscopy.
07, δ 15 N = -0. 96), IAEA-N-2 ammonium sulfate (δ 15 N = 20. 3 ± 0. 2), and IAEA-600 caffeine (δ 13 C = -27. 771 ± 0. 043, δ 15 N = 1. 0 ± 0. 2)) were used for isotopic corrections, and to assign the data to the appropriate isotopic scale with analytical precision better than 0. 1‰. Percent C and N were calculated using additional acetanilide standards of varying mass. Corrections were done using a regression method. Results were reported in the δ unit notation and expressed as parts per thousand (‰) relative to international standards (atmospheric N2 for nitrogen and Vienna Pee Dee belemnite (VPDB) for carbon). Reproducibility of several samples measured in triplicate was <0. 2 ‰. For samples with elevated lipid content (C:N > 3. 5), δ 13 C values were corrected using a mass balance equation with parameters derived from Atlantic bluefin tuna muscle  (Logan et al.
Temporal structure of Hg concentrations was investigated only for yellowfin and skipjack as there was not enough data per season for bigeye. For potential ecological and environmental drivers of Hg concentrations, only long-term trends were investigated as no particular seasonal structure was revealed for both log(Hg) and standardized Hg concentrations. RDA followed by ANOVA like permutation tests were then fitted separately on tuna muscle δ 15 N and δ 13 C values, and on each environmental variable (SST, MLD, Chl-a, NPP, Diso12 and Diso20). For TP estimates and Sr-AA δ 15 N values, as few data were available, long-term trends were investigated using a linear regression fitted along years.
Mercury concentrations in tropical tuna Among the three species, only bigeye tuna exhibited Hg concentrations exceeding the food safety guideline of 1 µg. g -1 (wet weight) (WHO and UNEP Chemicals, 2008), representing 32% of the sampled specimens. Overall, most of these individuals (86 %) were bigger than 110 cm. A few yellowfin were also above the food safety guideline but represented only 0. 6% of the dataset. This illustrates the need to consider both tuna species and fish size when addressing recommendations in terms of food security regarding Hg content. Relative differences of Hg concentration between the three studied tuna species in the south western Pacific Ocean were similar to those reported in the north eastern and north western Pacific Ocean  (Blum et al. , 2013; Choy et al. , 2009; García-Hernández et al.
Decadal stability of tuna mercury concentrations Our study revealed no significant long-term trend of Hg concentrations between 2001 and 2018 in the New Caledonia-Fiji region despite significant inter-annual variability (discussed separately below). As samples were collected opportunistically onboard fishing boats, it was not possible to perform our temporal analysis on the same number of samples for each species. The most complete and continuous yellowfin dataset illustrates the absence of a time trend over the 18 years, and similar finding applies to both skipjack and bigeye tuna. Our observed stable long-term trends contrast with the estimated Hg increases of 3. 8% annually reported in yellowfin tuna caught near Hawaii from 1971 to 2008  (Drevnick et al. , 2015)  and the mean annual decreasing rate of 19% from the 1990s to the early 2000s found in the Atlantic bluefin tuna from the north western Atlantic Ocean  (Lee et al.
, 2020). Our contrasted results with the two other temporal studies of Hg concentrations in tuna, located in the northern hemisphere, could be also related to distinct methodological approaches used to investigate the temporal trend over time. Drevnick et al. (2015)  performed their temporal analysis on distinct, and highly heterogeneous datasets covering only three years  (1971, 1998 and 2008)  over a long study period (37 years), and with a limited number of individuals for the most recent period (n=14 in 2008). Conversely, we used a quasi-continuous long-term dataset analysed in the same laboratory from 2001 to 2018 with a larger sample size and more powerful statistical tools. Finally, unlike our study, no ecological proxies were available in these two other temporal studies of Hg content; therefore, it was not possible to investigate the potential confounding ecological contribution to the decreasing or increasing long-term trends of tuna Hg concentrations.
, 2013; Kwiatkowski et al. , 2017) , it has become necessary to assess the predictive capacity of environmental variables on Hg methylation and bioaccumulation to understand potential climate-driven ecological changes. With a continuous and long-term dataset, we revealed for the first time the absence of significant decadal trend of Hg concentrations in tropical tuna from the New Caledonia-Fiji region. This contrasts with the two other available temporal studies from distinct areas  (Drevnick et al. , 2015; Lee et al. , 2016)  and thus illustrates the complexity of the Hg cycle and the fact that Hg in tuna does not necessarily follow the suspected recent increasing Hg trends in global oceans, especially in the southern hemisphere. Our results however seem consistent with the remoteness of our study region in terms of low anthropogenic emissions, and thus seem to confirm the hypothesis of distinct hemispherical ocean patterns of Hg anthropogenic deposition.
Acknowledgments We thank the large team of observers and supervisors from the  National Observer Programs of the Pacific Island Countries and Territories and FSM   Arrangement Observer Program  who collected the tuna samples. We are grateful to the  WCPFC (Western and Central Pacific Fisheries Commission) Tuna Tissue Bank  and the  SPC (Pacific Community )  Pacific Marine Specimen Bank  which gave us access to the tuna tissue samples. We thank the  LAMA laboratory , especially  Jean-Louis Duprey  and  Stéphanie Berne  for Hg analyses, as well as the  Union College Stable Isotope Laboratory team Anouk Verheyden ,  Sarah Katz , and  Madelyn Miller. We also thank  Pablo Brosset  for his helpful discussions. Finally we thank the four anonymous referees for their remarks and suggestions that substantially improved the manuscript.
Feature selection Feature selection was suited for each data type (MS and HS). Principal component analysis (PCA) is a feature extraction method often used for the reduction of spectral bands. PCA reduces redundancy across features by projecting them into a lower-dimensional space in which the variance over the dataset is preserved  (Jolliffe, Cadima, 2016). In our case, PCA was exploited both on time series of MS images, by computing it for each date, and on the HS image. The resulting features were a group of virtual bands highlighting variance over vegetation. Their variations underlined phenology changes with multitemporal exploitation  (Macyntre et al. , 2020). Components with higher noise levels were removed by visual inspection. 10, 20 or 40 principal components were retained for the HS image.
Performance assessment Application to all classes The overall performance was greatly increased by limiting the date number to build the time series or extracting spectral characteristics. The seasonal time series was created by selecting one date per season (03/10 for spring, 06/18 for summer, 09/23 for autumn and 12/25 for winter). During classification over the entire area, this date selection provided results 22 to 28% higher than those obtained with monthly time series. The progress achieved by extracting spectral features led to even better results, reaching up to 38 %. However, no progress was observed by combining temporal selection and spectral extraction (seasonal PCA, seasonal indices) on the entire dataset. Neither spectral feature extraction method outperformed the other. Nevertheless, combining spectral vegetation indices conducted to higher accuracies than exploiting a single vegetation index (performance increase around 10 %).
Software and algorithms Bowtie 2  (Langmead and Salzberg, 2012)  http://bowtie-bio. sourceforge. net/ bowtie2/index. shtml SCN normalization procedure (Cournac et al. , 2012) https://github. com/koszullab/hicstuff R ( RStudio Team, 2020) https://rstudio. com Chromosight (Matthey-Doret et al. , 2020) https://github. com/koszullab/chromosight HTseq -count (Anders et al. , 2015) https://github. com/htseq/htseq DESeq2 (Love et al. , 2014) https://bioconductor. org/packages/release/ bioc/html/DESeq2. html SAMtools (Li et al. , 2009) http://www. htslib. org bamCoverage (deepTools) (Ramı ´rez et al. , 2016) https://github. com/deeptools/deepTools/ blob/master/docs/content/tools/bamCoverage. rst Deposited data Codes and functions for Hi-C analysis (Matthey-Doret et al. , 2020) https://github. com/koszullab/hicstuff.
Data and code availability The datasets generated during using protocol are detailed in  (Cockram et al. , 2020)  and are available at PRJNA587586. Open-access versions of the programs and pipelines used for downstream data analysis (E. coli analysis pipeline and Chromosight; Matthey-Doret et al. , 2020)) are available online via the lab Github account (https://github. com/koszullab/hicstuff).
Methodology To quantify the amount of the corresponding global gridded daily and monthly changes, the adjustment factors (AFs) are determined following the general methodology schematized in Fig. 1. The four different steps taken in our study to estimate the AFs are the following: (1) a data survey is first carried out to collect activity data for the power gen-Earth Syst. Sci. Data, 13, 4191-4206, 2021  https://doi. org/10. 5194/essd-13-4191-2021 eration, industrial processes, residential, road transportation, shipping and aviation sectors. These sectors correspond to those considered in many global inventories such as the CAMS global anthropogenic emission inventory (CAMS-GLOB-ANT_v4. 2_R1. 1; Granier et al. , 2019). The activity data used to estimate the emission AFs are available from numerous sources for different timescales, depending on the geographical area.
It should be noted that, in several regions of the world, accurate and/or up-to-date data are not publicly available, which could lead to a significant source of uncertainty in estimating reduction rates. Table  1  indicates the availability of data for the different sectors. Some of these data are used to support our analysis and contribute to the estimation of uncertainties in the AFs; (2) the collected data are then analyzed, and an intercomparison of the changes in activity data of datasets providing similar or equivalent parameters is performed. The dataset that provides the most detailed information and which better meets our needs is then chosen. The non-gridded AFs are calculated for each country and state/province according to the availability of activity data, as detailed in the following sections; (3) the gridded daily and monthly NetCDF files developed for each sector are obtained by assigning to each cell the value of the AFs in the whole country or state/province level corresponding to this grid cell: this is based on the fact that the lockdowns and restrictions have been generally taken at national or state level.
Industrial processes This sector includes industrial production processes such as manufactured products from fossil fuel combustion and represents a significant part of the emission sources of atmospheric pollutants. The 2020 data concerning industrial production are, however, not publicly available for many countries and regions. We used the crude steel production from the World Steel Association (Table  1 ), which is provided on a monthly basis, to estimate the rate of change in the industrial sector. Due to the difficulties to access the daily data in this source category, we assumed that changes in Google's workplace measures, which represent the percentage of people traveling to or from their workplaces, are representative of changes in industrial activities during the lockdowns. To verify our hypothesis, we compared the calculated monthly average AFs based on Google's workplace data for selected countries with those derived from crude steel productions (Fig.
Air transportation and shipping We have used the monthly data on air transportation published by the Knowledge Center on Migration and Demography (KCMD) Dynamic Data Hub (Table  1 ). In addition to the observed passenger volumes from around the world, the KCMD provides air traffic scenarios covering the COVID-19 period. Five scenarios are provided  (Iacus et al. , 2020). We selected the scenario called EUROC-L (L-shaped version) based on the EUROCONTROL air traffic data, which provides global up-to-date monthly average air volumes. To assess the representativeness of the KCMD estimates, we compared the EUROC-L scenario data with the number of scheduled international flights from 14 countries in 2020 provided by the OAG (Official Aviation Guide) (https: //www. oag. com/coronavirus-airline-schedules-data, last access: 12 August 2021).
Version R. 1 of the CAMS-GLOB-ANT_v4. 2 dataset incorporates the MEIC1. 3 regional inventory for China described by  Zheng et al. (2018). The percentage of global change in emissions during COVID-19 compared to the reference 2020 emissions is https://doi. org/10. 5194/essd-13-4191-2021 Earth Syst. Sci. Data, 13, 4191-4206, 2021 shown in Fig. 7  for the main pollutants (NO x , CO, SO 2 , BC, OC and NMVOCs) from January to June 2020. The vertical bars indicate the estimated lower and upper limits of the changes in emissions due to the restrictions. Figure  7  shows that the changes depend on the chemical species, with decreases in monthly global emissions of 22 % (11 %-26 %) for NMVOCs, 17 % (12 %-24 %) for NO x , 14 % (7 %-18 %) for CO, 10 % (6 %-18 %) for SO 2 and 9 % (3 %-13 %) for BC in April 2020 compared to a non-COVID-19 scenario.
In agreement with the changes in the activity data previously shown, the reductions are highest in February in the eastern China region for all species except OC, for which a relatively small increase is observed during that month. The average monthly NO x emissions in eastern China derived from this study decreased in February 2020 by 29 % (24 %-37 %) compared to the reference emission scenario (i. e. , without a COVID-19 effect). The NMVOC emissions decrease significantly, by 22 % (15 %-29 %). The decreases in the amount of SO 2 emissions are of the same order of magnitude. BC and CO show maximum average reductions of 8 % and 10 %, respectively, in the eastern China region, while OC shows a slight increase of 1. 6 % for February. In the rest of the world, NO x emissions exhibit large decreases (13 %-42 %) during the strictest shutdown period (i.
Statistical Analysis All statistical analyses were conducted using SYSTAT, version 13, and graphical data presentations were made using SigmaPlot for windows, version 14. (Systat Software, Inc. , San Jose, CA, United States). All data are presented as mean ± one standard deviation (SD). Whether data fitted the assumptions for parametric analysis, i. e. , normally distributed data with homogeneous variances, was tested using Kolmogorov-Smirnov and Levene's test, respectively. One-way Analysis of Variance (oneway ANOVA) was conducted when data met the requirements for parametric tests. Tukey's Honestly Significant Difference Test was used when significant differences were detected to determine significant pairwise differences between samples. When data (or transformed data) did not meet the requirements for parametric analysis, a nonparametric Kruskal-Wallis analysis on ranks was performed, followed by Conover-Inman Test for all pairwise comparisons.
Reproductive Effects The mean number of cocoons produced during 28 days of exposure in freshly spiked sediment was about 15 for T. tubifex in all treatments. Hatching efficiency after 2. 5 months after exposure termination was generally about 75% and above 60% in all treatments. There was a tendency for increased hatchability with increasing exposure concentration in both 65 Cu treatments. However, there was no significant effect of 65 Cu sediment concentration on the number of cocoons or hatching efficiency for either 65 CuCl 2 (p 0. 532 and 0. 223, respectively; Kruskal-Wallis) or 65 CuO NPs (p 0. 929 and 0. 301, respectively; Kruskal-Wallis) (Figure  6 ). Thus, limited influence 65 Cu treatment or exposure concentration on T. tubifex reproduction was observed. Reproduction was only assessed for experiments in freshly spiked sediment and therefore no data are presented for reproduction in aged sediment.
ACKNOWLEDGMENTS The authors are grateful to the  Villum Foundation  for financing the research (Nanotransfer Grant  00010592 ) and to  Xianjin Cui  and  Eugenia Valsami-Jones  for providing 65 CuO NPs. We would like to thank  Mette Flodgaard  for conducting ICP-MS measurements and assistance during the work and  Rikke Guttesen  and  Anne Busk Faarborg  for assistance in the laboratory.
Indeed, the velocity of water movement inside the earth plaster decreases with time, and it becomes sufficiently slow after 3 min in order to allow a reduction of frame speed acquisition without any prejudicial loss of information. After several preliminary tests with pure distilled water, potassium iodide salt was added to the water at a 5% concentration. It increases the attenuation of the signal by the liquid, increasing the gray level difference between the dry earth plaster and material containing water. The presence of water in the earth is then easier to detect by the decrease of the gray value of the pixels in the material. A first program was developed on MATLAB to detect the position of the water front in the earth plaster, allowing the measurement of the depth penetration and the spread of the droplet during the time.
1 mm from the surface of the plaster since the upper side of the ROI corresponds to the surface of the sample. To measure the spreading, the same procedure is used, but the ROI is selected just under the surface of the sample where the droplet impacts, and the calculation is made for vertical lines instead of the horizontal ones. The results here indicate the size of the spread over the time. An example of this ROI is given by the green frame on the Fig. 2. Most of the time, the threshold was equal to 0. 02% of base gray value from the dry zone. However, for some samples where the noise was too important, the threshold has been set to 0. 03%. For different samples, the spread was measured with these two values of threshold, leading to a 2% maximum error on the measurement. A second MATLAB program was developed to follow the relative quantity of water inside the material.
The selected ROI, described on Fig. 4 , needs to surround all the penetrated droplet, then the mean of the gray level of each pixel of the ROI is calculated. This mean value is calculated over the time and compared to the initial mean gray value, giving a gray value variation over the time. This allows the observation of the drying phenomenon, and its speed rate. Because the average gray value of the picture drift slightly with time, due to instability of the X-Ray source, a small ROI inside the material is selected, at a location where it is certain that there cannot be water. In this ROI the same calculation is made, and the   result, assimilated to a slide, is subtracted to the main gray level variation. With this addition, this code leads finally to the variation of the total gray value of the droplet in the material, which can be representative of the water mass.
Pore size determination Table  6  presents three levels of porosity. First n pycno is the total porosity of the material, determined experimentally with the pycnometer. However, this technique does not give any information about the pore size distribution. Consequently, a custom MATLAB code has been designed in order to obtain the porosity of the sample from a 3D image. The porosity detected with this method is noted n tomo. Since the pixel size for the experiment was set at 20 mm, it is the smallest size of pore that this program is able to detect. Finally, the amount of small pore n s (<20 mm) is the difference between n pycno and n tomo. The porosity n tomo is quite small compared to n pycno , as only pores larger than 20 mm are considered. The largest porosity n pycno was for A1 and A3 sample, due to the worst particle size stacking, stable around 26% for the other ones, and lowest for STA and Con-fluence earth (samples B1C and B1S).
Acknowledgements. We thank  Total Exploration and Production for sponsoring Jean-Philippe Blouet 's PhD, for granting permission to publish this research, and for many insightful discussions. In particular,  Eric Gaucher  is thanked for his constructive comments on the  SE France "carbonic province ", and we are grateful to  Stéphane Teinturier  for preliminary fluid inclusion analysis. The field campaign was made possible by  Philippe Artru , who guided us to the outcrops where he conducted his PhD work and first discovered the Aurel pseudobioherm back in the 1960s. Philippe Artru also brought us many original ideas on the regional geology of a basin on which he conducted research for some 10 years of his life. Rolland Oddou , a distinguished collector of septarian concretions from the  Terres Noires Formation , generously shared his extensive knowledge of the regional outcrops with us.