dataesr/scientific-paragraphs-categorization huggingface dataset:
The variability of precipitation plays a key role in determining the hydrological response of river basins. We assume that the river streamflow follows the variability of rainfall and we propose to use a hydrological model to evaluate the performance of multiple and distinct precipitation products. This study aims to contribute to the debate on the performance of satellite precipitation estimates that are based on different techniques. To achieve that, specifically this study aims to (i) analyze and intercompare the precipitation distribution characteristics from 23 satellite-based precipitation products over the CRB during the period from 2001 to 2012 from FROGS database; (ii) evaluate the selected satellite precipitation products' skills in simulating the streamflow and water balance components through a hydrological model, SWAT  (Soil & Water Assessment Tool, Arnold et al.
Satellite-Based Precipitation Datasets Twenty-three precipitation products, with a common period from 2001 to 2012 (except 3B42 v7. 0, period from 1998 to 2012), were selected for this study (Table  1 ). These datasets were acquired from The Frequent Rainfall Observations on GridS (FROGS) database  (Roca et al. , 2019). FROGS is composed of several quasi-global and regional daily precipitation products with a common spatial resolution of 1° × 1°. Although FROGS database includes satellites, ground-based and reanalysis products, only the satellite-based category was considered for this study. The satellite-based precipitation products, which differ from each other in multiple aspects (e. g. , satellite source, with or without gauge adjustments, among others), are here subdivided into two main groups: i) the satellite-only and ii) the gauge-calibrated satellite.
These products are retrieved utilizing infrared (IR) observations from geostationary satellites and/or passive microwave observations from multiple or single low elevation orbiting (LEO) satellites. The gauge-based calibration step is distinctly applied to those satellite precipitation products. These adjustments are climatological on a monthly or daily time step and take into consideration in-situ observations from the Global Telecommunication System (GTS) or other gauged-based precipitation products (e. g. , GPCC). Table  1  shows the satellite-based precipitation products selected for this study and their main characteristics. FROGS database is freely available from ftp://ftp. climserv. ipsl. polytechnique. fr/FROGs/. See  Roca et al. (2019)  for a detailed description of each product. Table  1.
2. 3. 1. Model Setup SWAT2012 version (Winchell et al. , 2013)  was set up for the CRB using the datasets listed in Table  2. The basin was delineated based on the dominant land use, soil and slope classes taking into cognizance the size and spatial heterogeneity of the basin allocating one Hydrologic Response Unit (HRU) per subbasin resulting in 272 subbasins and HRUs. The period of simulation was from 1998 to  2012, comprising calibration (2000-2006), validation (2006-2012) , and a two-year warm-up period  (1998) (1999) (2000). In the SWAT model, the evapotranspiration is calculated using the Penman-Monteith method. The Penman-Monteith method also gave better estimates of evapotranspiration when used with the plant growth modification of  Strauch and Volk (2013). The plant growth module was used since the SWAT model simulates plant growth based on dormancy during the winter season for the reinitiation of the growing season for perennial plants and this process is not a valid growing pattern in the tropics.
Evaluation of the performance of precipitation datasets using SWAT model The SWAT model was run for twenty-three scenarios based on the precipitation products described in Table  1. The base scenario is based on the 3B42_v7. 0 product and it was calibrated with observed streamflow (see above). The others precipitation scenarios were run using identical datasets (see Table  1 ) so that variations in streamflow and other hydrological components were uniquely attributable to precipitation. For the evaluation of the performance of precipitation datasets, we used KGE and PBIAS objective functions calculated using the streamflow of base scenario (3B42_v7. 0) as reference. Finally, we discuss the influence of the precipitation input on model performance when the model has been pre-calibrated with one of the products (here 3B42).
Comparison of the different precipitation datasets Satellite precipitation estimates, through different gridded products, has been constantly evaluated and assessed across West Africa, considering gauged-precipitation observations as a reference (e. g. ,  Gosset et al. , 2018 , Nicholson et al. , 2019 , Satgé et al. , 2020). The reliability of satellite precipitation estimates depends on several factors (e. g. , seasonality, the gauge network densities) and can vary in space and time. The precipitation regime (i. e. , seasonality) over the CRB varies regionally (e. g. ,  Munzimi et al. , 2015). Spatially, there are remarkable characteristics on the precipitation distribution over CRB, which are demonstrated through the unconditional annual mean and the 99th percentile (Figure  2 ). The central region of the CRB and the northern portion of the Luabala sub-basin experience the largest amounts of precipitation with about 7 and 10 mm day-1, respectively.
This is because the precipitation regimes are driven by multiple factors, including the frequency and intensity of rainy/non-rainy days to its monthly totals, as well as the contribution of a certain class of precipitation occurrences and the corroboration of moderate-extreme and extreme events over tropical land regions  (Roca, 2019). These factors can be represented differently by satellite estimates and the differences are related to distinct aspects (e. g. , the product technique itself, platform sources, gauge-adjustments, systematic uncertainties, among others). Figure  4  shows the cumulative density functions (CDF) of occurrence and volume (amount) of daily precipitation during the period from 2001 to 2012 over the five Congo sub-basins computed for each satellite precipitation product (satellite-only and satellite gauge-adjusted) acquired from FROGs database.
Through the overall CDF analyses, it becomes more evident, the differences between the two groups of satellite precipitation compared to the 3B42_v7. 0 reference database. It is also possible to note that, even though the monthly totals are impacted differently, these two distinct CDF distributions are preserved for all the five watersheds. Better agreements between all the satellite-only products compared to the 3B42_v7. 0, in both the occurrences and amounts of precipitation distributions, were observed. Slight overestimations (underestimations) on the amounts (occurrences) frequencies of rainfalls lower (greater) than ~5 mm day -1 were observed from most of the satellite-only products compared to 3B42_v7. 0. However, the ARC2 and CHIRP_V2 products presented opposite features, especially at the Luabala and the Central basins respectively.
Preliminary Numerical Results A theoretical validation of the concept of the FMM technology was carried out through a numerical study. This section summarizes the main results, obtained and taken into consideration, to carry out the experimental study detailed in Section 4. All simulations are run through the 3D module in the Ansys software (student version), which uses the Finite Element Method (FEM) to solve the Maxwell equations. Being limited to a maximum amount of 64,000 elements for a 3D volume, we set the maximum meshing size with respect to the geometry of each elements that we simulate (4. 0 mm for the permanent part and 1. 5 mm for the squared-shaped reactive part). The software divides the total modeled volume into small sub-regions in order to run, both separately and for each of them, the Maxwell equations.
, San Diego, CA, USA). The dada2 package (v. 3. 9) in R studio interface (v3. 2. 3) was used to process the raw data by following a previously described workflow  35. The representative amplicon sequence variants (ASVs) were taxonomically classified against SILVA 16S rRNA gene reference database release 132  36. The ASVs that were taxonomically unclassified at phylum rank or taxonomically assigned to mitochondria and chloroplast were removed from the dataset. Diversity index (specific richness and Shannon index) were compared after rarefaction of the samples at an even number of sequences (2615 reads) with the phyloseq package (v3. 9)  37. Chemicals and reagents. Dichloromethane (DCM), hexane, ethyl acetate (EtOAc), acetone, methanol, and toluene, were purchased from Promochem (Picograde, LGC standard).
To determine the real OPE concentration after spiking (i. e. "T0"), the OPE concentrations at Ti were subtracted from T0. Finally, in order to minimize the artifact of excessive data correction, no blank corrections were performed for the degradation experiment (i. e. T0-T3). Regular measurements of microbial abundances on the abiotic sediments were performed to confirm the abiotic conditions during the 1-month experiment (no microbial growth), allowing to specifically assess the changes in the OPEs concentrations due to abiotic processes. The biotic controls used to monitor the effect of the incubation protocol on the prokaryotic community were spiked with 160 µL of the toluene/acetone solution (65:35, v/v) not containing OPEs to evaluate the solvent effect.
Acknowledgements Without implicating them, we thank Philippe Baret, Johan Le Douce, Antoine Messean, Hania Mezghrani, and Jarosław Stalenga for their help. This research has received funding from the European Union's Horizon 2020 research and innovation program under the grant agreement No 727482, DiverIMPACTS, and from the French Agence Nationale de la Recherche (ANR) under the grant agreement DIETPLUS ANR-17-CE21-0003. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors only are responsible for any omissions or deficiencies.
Taxon sampling, amplification and sequencing The selection of the P. perna specimens used in this study included one individual from each South African lineage (eastern and western) and one specimen from Santa Catarina, Brazil, (hereafter called B2) from the same geographic area as the specimen for which the mitogenome was previously sequenced (B1; NCBI GenBank accession number: KM655841). Rhodes University approved sampling of the eastern and western South African P. perna specimens (Research Reference Number: RES2019/30) and Federal University of Santa Catarina approved sampling of the Brazilian P. perna specimen under the MTA -Material Transfer Agreement, SISGEN (https://sisgen. gov. br/) registration number: RB435EFp. All specimens were preserved in 96% ethanol. DNA was extracted using the DNeasyÒ animal tissue QIAGEN kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions.
PCR products were cleaned using purified ethanol/sodium acetate precipitation and directly sequenced with the corresponding PCR primers. Sequencing was performed on an Applied Biosystems 3130xl Genetic Analyzer, using Sanger technology and the BigDyeÒ Terminator v3. 1 kit. Sequences of the complete mitochondrial genomes P. perna from this study were deposited in GenBank under the accession numbers OK576479 (Brazilian specimen B2); OK576480 (western South African specimen) and OK576481 (eastern South African specimen).
Gene annotation, phylogenetic analyses and gene rearrangements The approximate locations of the rRNA and protein coding genes were determined by aligning the obtained sequences against the mitochondrial genome of Perna canaliculus (NCBI GenBank accession number MK775558) using Geneious v. 7. 1. 4 (Drummond et al. , 2010)  with the option "highest sensitivity". Protein coding genes were confirmed by inferring open reading frames (ORFs), and by delimiting start and stop codons. "Cloverleaf" secondary structures of all tRNA genes were reconstructed manually upon localization in Geneious of the specific anticodons, and using the tRNAs of P. canaliculus as reference. To reanalyze phylogenetic relationships within Perna and investigate the putative polyphyly of the genus as claimed in (Uliano-Silva et al.
, 2016b), we used a dataset (nine taxa, 14,251 bp) that included the newly sequenced genomes and the mitogenomes of the mussels Perna viridis (JQ970425), P. canaliculus (MG766134), P. perna (KM655841), Brachidontes exustus (KM233636) and Musculista senhousia (GU001954). Following the results of (Uliano-Silva et al. , 2016b), we selected Limnoperna fortunei (KP756905) as the most appropriate outgroup. The deduced amino-acid sequences of each of the 13 protein-coding genes and the two ribosomal RNAs (12S rRNA and 16S rRNA) were aligned using Mafft v7. 245 (Multiple alignment using Fast Fourier Transform)  (Katoh & Toh, 2010)  using the-auto option that automatically selects the appropriate strategy according to data size. Refinement of the alignment was performed "by eye" to maximize positional homology.
The aligned sequences were concatenated into a single data set in interleaved Nexus format. The Akaike information criterion  (Akaike, 1973)  as implemented in jmodeltest-2. 1. 10 (  Darriba et al. , 2012; Guindon & Gascuel, 2003)  selected the GTR+I+Γ 4 (general time-reversible substitution model with a proportion of invariant sites and among-site rate variation modeled by a discrete gamma-distribution with four categories) as the evolutionary model that best fitted the dataset. The best partitioning schemes were identified by PartitionFinder2 v. 2. 1. 1  (Lanfear et al. , 2017)  and used in maximum likelihood (ML) analysis. Our ML and BI analyses included P. canaliculus, P. viridis and four P. perna lineages (two specimens from Brazil and two from South Africa). Musculista senhousia and Brachidontes exustus were also included to compare with results from Uliano-Silva et al.
(2016b). We performed the ML analysis of the 13 protein-coding and the two ribosomal genes with RAxML v8. 2. 10 (Stamatakis, 2014) using the options -M that switches on estimation of individual per-partition branch lengths andq which specifies the file name which contains the assignment of models to alignment partitions for multiple models of substitution under the GTRGAMMA (gamma model of rate heterogeneity). The best-scoring tree was determined from 100 randomized maximum-parsimony starting trees using the rapid hill-climbing algorithm. 500 bootstrap replicates were drawn on each best-scored ML tree using the exhaustive bootstrap algorithm. Bayesian Inference was conducted with MrBayes v. 3. 2. 7a  (Ronquist et al. , 2012 ) under the selected model (lset nst = 6 rates = invgamma;) using the same dataset as in ML analysis.
, 2016b), we performed a nucleotide BLAST search (https://blast. ncbi. nlm. nih. gov/) using the cytochrome oxidase subunit I (COI) gene of the published Brazilian mitogenome B1 to find the closest matching sequences in NCBI GenBank database. COI is the most common gene for DNA barcoding and widely used for species identification. The BLAST search indicated that the COI from the Brazilian mitogenome of P. perna (B1) is 100% similar to Mytilaster solisianus (synonymized with Brachidontes solisianus) and there was no match with any other Perna species. The two Brazilian P. perna mitochondrial genomes (B1 and B2) differed in size (Fig. 2 ). While our sample B2 (16,065 bp) is similar in length to the other two Perna species (P. viridis: 16,014 bp and P. canaliculus: 16,065 bp), B1 is 2,350 bp longer (18,415 bp).
The assembly of B1 was performed using as reference the P. viridis mitogenome (NC_018362), while we used P. canaliculus, which is the sister species of P. perna. We were not able to perform a new assembly of B1 using P. canaliculus as reference to analyze if gene order would change or to confirm that ATP8 was not present, as raw reads are not available in either public databases or from the authors.
ACKNOWLEDGEMENTS We are very grateful to  Paulo A. Horta  and  Paulo R. Pagliosa  for collecting the sample of Perna perna from  Brazil  and dealing with all the required permits. We also thank  Luana Corona  for helping in the laboratory. Phylogenetic analyses were performed on the R2C2 computational cluster facility provided by the  IT Department of the University of Algarve  and on the CCMAR computational cluster facility (CETA).
Funding Regina L. Cunha  was funded by the transitional norm-DL  57/2016/CP1361/CT0013. This study received Portuguese national funds from  FCT-Foundation for Science and Technology  through project  UIDB/04326/2020 , and from the operational programmes CRESC Algarve 2020 and COMPETE 2020 through projects  EMBRC. PT ALG-01-0145-FEDER-022121  and  BIODATA. PT ALG-01-0145-FEDER-022231. This research was further supported by the  Foundation for Science and Technology (FCT-MEC, Portugal  (Grant Number:  EXPL/BIA-BMA/0682/2021 )), and the  National Research Foundation of South Africa  (Grant Number:  64801 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements This study was financially supported by the  Natural Sciences and Engineering Research Council of Canada Discovery  and Accelerator grants (B. S. L. ) and  Nuclear Waste Management Organization of Canada  grants (B. S. L. and C. J. B. ) with additional funding by  CIFAR  to Earth 4D Fellows B. S. L. and C. J. B. , and  University of Oxford  internal funds (C. J. B. ). Sampling infrastructure and operations funded via  NSF  grants  EAR1917682  and  EAR2026853 (T. L. K. and T. C. O. ). ICDP grant  to  H. Ogasawra  of  Ritsumeikan University, Japan  and the DSeis international team supported the cost of drilling the borehole at  Moab Khotsong Mine. NSF  grant  EAR1739151  to T. C. O. supported the construction and installation of the U-tube in the borehole. We thank  B. Liebenberg  and the staff of  Lesedi Drilling & Mining (Pty) Ltd and Van Heerden Esterhuizen and Brenda Freese of Moab Khotsong Mine  and the management of  Harmony Gold Mining Company Ltd.
Macronutrients Content Results of Micro-Tom tomatoes macronutrients content are presented in Figure  1. Fruits samples were collected from hydroponically grown plants. The control condition with iron, Fe, and nitrogen, N, (i. e. , +F/+N) is reported in Figure  1  under ambient (400 ppm) or elevated (900 ppm) atmospheric carbon dioxide (CO 2 ). Data indicated that Micro-Tom cultivated under elevated CO 2 concentrations (eCO 2 ) contained a significantly lower concentration of sucrose, total proteins, and total lipids. Conversely, the same fruits contained a significantly higher concentration of fructose and glucose. From a statistical point of view, the major differences were recorded for sucrose, total protein and lipids (reduction), and glucose (elevation).
, 2011)  which has been recently associated with a range of statistical methods called point process models  (Renner et al. , 2015). The diversity of possible algorithms for SDMs makes the selection of candidate algorithms a difficult decision, especially in presence-only situations where it is notoriously difficult to estimate model performance reliably. The field of biogeography was sorely lacking a comprehensive and reproducible evaluation of the performance of the currently available algorithms. To fill this gap, Valavi and colleagues used a massive benchmark dataset composed of 225 species with presence-only records from six different regions of the world. This dataset has several features F I G U R E 1 Schematic representation of the major steps of the process of building a species distribution model (SDM) and the multiple decisions that users have to make at all steps.
First and foremost, it has independent presenceabsence data to evaluate the presence-only models for each species. Independent presence-absence data for model evaluation are the most appropriate and arguably the rarest type of evaluation data in presence-only studies. This first feature itself sets this comparative study as a reference in the field for the choice of presence-only modelling techniques, since no other study has been published on this topic with a similar or better evaluation dataset. Second, the environmental conditions and taxonomic groups in this dataset are diverse. This diversity probably covers a large breadth of relationships between species and their environment, and thus the results could be transferred to a wide range of taxa with similar species-environment relationships.
Third, the dataset covers a gradient of data in quantity (from five to thousands of records) and quality (biased and unbiased). Hence, the results of Valavi and colleagues help modellers to decide on which techniques are adequate for their study, by pinpointing the techniques which perform best under data-poor or data-rich conditions, and those that perform well in all conditions. Valavi and colleagues reached conclusive results on the relative performance of the 21 tested modelling techniques. I do not intend to mention all their important results, but rather, I want to illustrate with some examples how the findings are likely to improve future practices. For example, they established how the choice of default settings over carefully tuned models can be extremely detrimental to model performance, which is the case of the machine-learning technique called Random Forests.
AERONET data The AERONET (AErosol RObotic NETwork) photometers measurements  (Holben et al. , 2001 (Holben et al. , , 1998)) , are used to characterize the observed AOD). The AOD data are recorded by numerous stations deployed around the world and hourly values are available in https://aeronet. gsfc. nasa. gov/new_web/aerosols. html. Several quality levels are proposed on the AERONET database. In this study, the level 2. 0 AERONET AOD at 440 and 870 nm and ngstrm exponent (AE 440/870 )  (Dubovik and King, 2000)  with an uncertainty estimated to less than 0. 02  (Majdi et al. , 2018; Holben et al. , 1998)  were used to derive AOD at 550 nm using logarithmic interpolation. If AOD value is not avaliable at the 440, then AOD is not obtained at 550 nm. If the AOD at 870 nm is not available, the iterpolation is made between AOD at 440 and 1020 nm.
AIRBASE data AirBase (https://www. eea. europa. eu/data-and-maps/data/airbase-the-europeanair-quality-database-8) gathers regulatory data reported by Member States of the European Union according to the air quality directives  (AirBase, 2015). For this study, quality-controlled and assured hourly PM 2. 5 data from both rural and urban background stations have been used. Figure3 shows the locations of the AirBase stations over Europe used here.
Simulations experiments evaluation Two simulations are performed with the same input data and parameterizations during the month of March 2009 which correspond to record pollution   The effect of OA during the one-month period is significant. The main differences between the modeled concentrations of PM 2. 5 and AOD with and without OA are located over France (∼50-60%) and the southern part of Europe (∼50-60%): the northern part of Italy, Balkans, south of Greece and over the Mediterranean. The modeled PM 2. 5 concentrations (resp. AOD) are compared against hourly surface observations from AirBase (resp. AERONET) network. Using the default simulation without the DA, the modeled surface concentrations of PM 2. 5 are significantly underestimated by a factor of about 47%(the measured and modeled means are 38.