dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 2, R Foundation, Vienna, Austria) and the package "BlandAltmanLeh"  (Lehnert, 2015). This approach illustrates on the x-axes mean values of e. g. isotope ratios obtained with two different analytical methods and on the y-axes the absolute difference of these values. The graphical output of this approach enables to inspect trends between the two analytical techniques (e. g. higher isotope ratios deviate more than low isotope ratios) while the data output gives the mean (bias) and the 95% confidence interval of the absolute difference of the two analytical methods  (Altman and Bland, 1983; Giavarina, 2015; Jarosch et al. , 2015). Differences in mean 67 Zn abundances in the plant available soil Zn pool and in the labeled composts between Q-ICPMS and the MC-ICPMS analyses were tested using a pairwise t-test with Bonferroni p-value adjustment.
, 1997; Fisher and Harris, 2010; Le Gal et al. , 2018). Due to the structural weakness and permeability of transform faults and fracture zones, significant seafloor heat flow anomalies and possibly hydrothermal activity are to be expected. However, only very few detailed heat flow surveys across a fracture zone or a transform fault have been made up to now  (von Herzen et al. , 1970; Kolandaivelu et al. , 2017). Published data from  Khutorskoi and Polyak (2017)  from the Vema fracture zone in the equatorial North Atlantic indicate remarkably uniform and high heat flow, with values between 100 and 250 mW m -2 (coinciding with similar results by  Langseth and Hobart, 1976). Rather than focussing on MTFFZs, most of the offshore data from the Global Heat Flow Database (data from 1965 to 1985; Figure  7 ) were collected in order to characterize the marine heat flow pattern in conjunction with plate tectonics, and hence the cooling of the oceanic lithosphere.
The model of  Roland et al. (2010)  was applied to the Blanco Transform Fault (Northeast Pacific) and the results are constrained by maximum earthquake depth, which the authors associate with the 600 • C isotherms. Gerya (2012)  and  Gerya (2013a,b)  reviewed the modeling of ridge segmentation and hence the development of transform faults and fracture zones. The emphasis of their modeling attempts is, however, on the seafloor expression of the underlying processes and the comparison with the observed bathymetry. Overall, the presented literature and database search revealed that heat flow investigations at transform faults and fracture zones are a neglected research field. Yet, none of the published numerical models calculate seafloor heat flow, which would be, apart from the maximum depth of earthquakes, another helpful constraint for the models.
ACKNOWLEDGMENTS The authors wish to thank all members of the FLOWS consortium (https://www. flows-cost. eu/de/members)for fruitful discussions and ongoing knowledge exchange. Additional thanks for fruitful collaboration go to colleagues of the InterRidge working group on Oceanic Transform Faults (https://www. interridge. org/WG_Transform_Faults),the  Deep Carbon Observatory  (https://deepcarbon. net/), and the  MARSITE Programme [EU Grant  agreement N • (308417)]. The manuscript benefited from critical and insightful comments by  Shinsuke Kawagucci  and  Wolfgang Rabbel. JD  acknowledges an  FCT Researcher contract , an exploratory project grant  ref. IF/00702/2015 , and the  FCT  project  UID/ GEO/50019/2013-IDL. LG  acknowledges the bilateral ANR/TÜBITAK collaborative research project  MAREGAMI  ( ANR-16-CE03-0010-02  and  Tübitak  Project  116Y371 ).
(2014)  showed it had a higher power than the mtDNA-CR to discriminate among distinct lineages. Using this dataset, we reassessed the genetic evidence of population subdivision previously reported based on phenotypic  (Gol'din and Vishnyakova, 2015, 2016)  or mtDNA-CR variation  (Viaud-Martinez et al. , 2007; Tonay et al. , 2017). Using the microsatellite dataset, we also tested whether relatedness rather than population subdivision could account for the previously reported genetic distinctiveness of the Marmara porpoises compared to the others  (Viaud-Martinez et al. , 2007; Tonay et al. , 2017). Finally, we built a theoretical framework to interpret an absence of genetic structure, and decipher the possible hypotheses which would explain such panmixia (i. e. , limited power, population "grey zone" effect, or panmixia).
Sampling and data collection The samples used in this study originated from five geographic locations: the Aegean Sea, Marmara Sea, Black Sea, Kerch Strait, and Azov Sea (Fig. 1 , Table  1  and S1 ). Genotypes at 10 microsatellite loci for 89 porpoises from the Aegean, Marmara, and Black seas were taken from  Fontaine et al. (2012). We added 55 newly genotyped individuals from the Azov Sea, Kerch Strait, and Black Sea. The final dataset included 144 individuals covering the complete subspecies range (Azov Sea: N=32, Black Sea: N=87, Marmara Sea: N=3, Aegean Sea: N=11, Kerch Strait: N=3, and four individuals of unknown locations) (Fig. 1  and Table  1 , and for details see Table  S1 ). The new tissue samples were collected from dead animals stranded along the coasts of the Black Sea, the Crimea peninsula (Ukraine), the Kerch Strait, and the Azov Sea, and kept in DMSO until analyses.
Total genomic DNA was extracted from tissues using a PureGene and DNeasy Tissue kit (Qiagen), following the manufacturer's recommendations. The microsatellite genotyping procedure followed the protocol described in  Fontaine et al. (2006; 2007). In addition to the nuclear microsatellite dataset, we sequenced a 3,904 base-pair fragment of the mtDNA genome encompassing five coding regions (CytB, ATP6, ATP8, ND5 and COXI) for 10 individuals (Azov Sea: N=6, Black Sea: N=3 and Kerch Strait: N=1) and combined it with the 12 sequences previously obtained for porpoises from the Black Sea in  Fontaine et al. (2014) , following the same protocol. Since the porpoises from the other locations were surveyed using the mtDNA-CR in previous studies  (Viaud-Martinez et al. , 2007; Fontaine et al. , 2012; Tonay et al.
Relatedness Considering closely related individuals to delineate population genetic structure can generate spurious signals of population structure and violates the assumptions of population genetic approaches, such as the model-based Bayesian clustering  (Anderson and Dunham, 2008; RodriguezRamilio and Wang, 2012). Therefore, we used the microsatellite data set to analyze patterns of relatedness among individuals using the R package related v. 1. 0 (Pew et al. , 2015)  in the R statistical environment v. 3. 5. 3  (R Core Team, 2019). Specifically, we estimated the relatedness coefficient (r) among individuals and tested whether it was greater within each location than expected by chance. As the performance depends on the characteristics of the data set  (Csilléry et al. , 2006)  and on the estimators, we compared seven estimators implemented in the related package following the user-guide recommendation using the function "compareestimators()".
This approach generates 1000 simulated data sets with the same characteristics as the observed microsatellite dataset. Then, for each estimator, a Pearson's r correlation coefficient is computed between the observed and simulated values. Wang's (2002)  r estimator provided the best performance for our dataset (i. e. , highest correlation coefficient) and was thus chosen for the analysis. We assessed whether individuals within each location were more closely related to each other than expected by chance. To do so, we compared the observed r value in each location against the null distribution of pairwise average r generated by randomly shuffling individuals among populations for 1000 permutations while keeping the population size constant. If some individuals are highly related within a population, the observed r value is expected to be higher than the simulated r values obtained by permutations.
, 2015). We determined the best K value using (1) the log likelihood of the data for each K value, (2) the rate of change of K with increasing K  (Evanno et al. , 2005) , and (3) the visual inspection of newly created cluster as K increased. For steps (1) and (2) we used STRUCTURE HARVESTER v. 0. 6. 94  (Earl and vonHoldt, 2011). We also investigated genetic structure using a Principal Component Analysis (PCA) on the allele frequencies  (Jombart et al. , 2009). This analysis does not rely on any model assumptions and provides a complementary visualization of the genetic structure. This analysis was conducted in R (R Core Team, 2019) using the adegenet v. 2. 1 package  (Jombart, 2008; Jombart and Ahmed, 2011)  on centered data (i. e. , centering the mean allele frequency on zero), with missing data replaced by the mean value as recommended by the authors.
Genetic differentiation among populations For microsatellites data, we estimated the overall and pair-wise departure from HWE due to population subdivision using the  Weir and Cockerham's (1984)  FST estimator. The pair-wise comparisons were carried among pairs of geographical locations (i. e. , the Aegean Sea, Marmara Sea, Black Sea, Kerch Strait, and Azov Sea). The 95% confidence interval (95% CI) was estimated using 5000 bootstrap resampling with the DiveRsity v1. 9. 90 R package  (Keenan et al. , 2013). The significance was tested using an exact G-test  (Goudet et al. , 1996)  implemented in Genepop v. 4. 7. 0  (Rousset, 2008) , with default options. We used a Bonferroni correction to adjust the p-value to 0. 05 of the pair-wise comparisons to account for multiple comparisons. For mtDNA data, due to the absence of samples for other locations, we only quantified the genetic differentiation between porpoises from the Black and Azov seas using the Hudson's estimator of FST  (Hudson et al.
, 1992)  in DnaSP v. 5. 10. 01. Significance was tested with 10,000 permutations of Hudson's nearest neighbour distance Snn statistics  (Hudson, 2000)  in DnaSP. We also conducted an exact test on the mtDNA haplotype frequencies using Arlequin v. 3. 5. 2. 2  (Excoffier and Lischer, 2010). We assessed the statistical power of our markers to detect genetic differentiation given the observed genetic diversity and sample sizes using the POWSIM v4. 1 program  (Ryman and Palm, 2006). POWSIM assesses whether the observed data set carries enough statistical power (i. e. ≥80%) to detect a Nei's FST (FST-Nei) value significantly larger than zero, using " 2 and Fisher tests  (Ryman and Palm, 2006). Parameters of the Markov chains, including the burn-ins, batches and iterations per run were set respectively to 10000, 200, and 5000.
Allele frequencies were estimated with GenAlEx and haplotype frequencies with DnaSP. Sample sizes were divided by two for the mtDNA to reflect the sampling of haploid genes  (Larsson et al. , 2008). Observed FST-Nei for microsatellite and mitochondrial data were calculated using DiveRsity v1. 9. 90  (Keenan et al. , 2013) , and the mmod v. 1. 3. 3 R package  (Winter, 2012) , respectively. Ne was fixed to 1,000 and the number of generations (t) was adjusted to obtain FST-Nei values ranging from 0. 001 to 0. 15 for microsatellites and from 0. 001 to 0. 4 for mtDNA.
007, Nei's FST=0. 013, Snn=0. 519, Snn's p-value=0. 726, and exact test on mtDNA haplotype frequencies p-value=0. 95). This suggests no mitochondrial genetic subdivision between porpoises from the Black Sea and Azov Sea. The simulation-based assessment of the statistical power to detect significant differentiation as performed in POWSIM (Fig. S4 ) indicated that our microsatellites and mitochondrial datasets have the power to detect significant differentiation for FST-Nei > 0. 008 and FST-Nei >0. 1, respectively (Fig. S4 ). Therefore, the lack of genetic differentiation observed at these loci among the five sampled locations does not simply result from a lack of statistical power.
2d ), and DAPC analyses (Fig. S3 ), and the absence of significant differences in genetic diversity (Table  1 ) and allelic frequencies (Table  2 ). The POWSIM power analysis showed that this homogeneity does not result from a lack of power of the microsatellite data to reject panmixia, since simulated datasets with the same number of markers and comparable genetic diversity would be able to detect significant FST values as low as 0. 008. Although sample sizes were small, mtDNA data also supported such homogeneity, with no differentiation between porpoises from the Black and Azov seas. Previous studies  (Viaud-Martinez et al. , 2007; Tonay et al. , 2017)  reported significant differences in mtDNA-CR haplotype frequencies between porpoises from the Marmara Sea and those from the neighboring areas.
Acknowledgments We thank  Diane Bailleul  for sharing the scripts to perform the simulations and  Jan Veldsing  for his assistance in the laboratory. We are also grateful to  Jeanine Olsen , three anonymous reviewers, and the editor for their constructive feedbacks which greatly improved the manuscript. We thank  Heïdi Lançon  ( Amphidae ) for her professional English editing services. We would like also to thank the  Center for Information Technology of the University of Groningen , and in particular  Bob Dröge  and  Fokke Dijkstra , for their continuous support and for providing access to the Peregrine high-performance computing cluster. This study benefitted from the funding of the  University of Groningen  (The Netherlands). YBC was supported by a  PhD fellowship  from the  University of Groningen.
Aerosol sources In IFS-AER, the sea-salt and dust emissions are computed dynamically using prognostic variables from the meteorological model. Conversion of sulfur dioxide into sulfate aerosol as well as nitrate production also uses input from the meteorological model. The other aerosol species use external emissions datasets. Aerosol emissions are released at the surface, except for emissions from biomass burning which can optionally be released at an injection height, and SO 2 emissions from outgassing volcanoes which can optionally be released at the altitude of the volcano. In the operational 45R1 context, emissions from biomass burning are released at the surface while SO 2 emissions from outgassing volcanoes are released at the altitude of the volcano.
Organic Matter and Black Carbon The anthropogenic (non biomass-burning) sources of OM and BC can be taken from the MACCity  (Granier et al. , 2011)  or the more recent CMIP6  (Gidden et al. , 2019)  emissions datasets; for the operational cycle 45R1 analysis and forecasts emissions from MACCity are used. These emissions inventories provide monthly emissions, updated from year to year for MACCity. MACCity emissions of black carbon are distributed by 20% into the hydrophilic and the remaining 80% into the hydrophobic black carbon tracers. MACCity emissions provide only organic carbon emissions rather than organic matter emissions. To translate these organic carbon emissions into OM emissions a OM:OC ratio of 1. 8 is used. This is in the middle range of the OM:OC ratio provided by  Canagaratna et al.
(2015)  and  Philip et al. (2014). The OM emissions are then divided evenly between hydrophilic and hydrophobic OM. Table  2  reports the average yearly global anthropogenic emissions for the year 2014 from the three inventories. Biomass-burning emissions from the Global Fire Assimilation System (GFAS) are also shown. The sulfur dioxide emissions are remarkably consistent between the three datasets. This is less the case for OM and BC. Biomass burning sources of OM and BC are provided by GFAS  (Kaiser et al. , 2012) , which estimates these emissions (along with those of trace gases) using active fire product from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Aqua and Terra satellites. Kaiser et al. (2012)  compared cycling forecast simulations of biomass burning aerosols with simulations using data assimilation and concluded that a scaling factor of 3.
Biomass burning emissions are by default released at the surface. This can be unrealistic: a large fraction of fires release smoke constituents in the planetary boundary layer (PBL) and a minority of very large fires emit large quantities of aerosols and trace gases in the free troposphere and even, for extreme cases, in the stratosphere  (Freitas et al. , 2005). The fraction of fires that emit aerosols and trace gases in the free troposphere was evaluated at 5-15% by various authors  (Kahn et al. , 2008; Val Martin et al. , 2010; Sofiev et al. , 2012). The GFAS dataset also includes daily injection heights that are computed using two different methods: the IS4FIRE approach  (Sofiev et al. , 2013)   Injection heights from GFAS as estimated using the PRM can optionally be used for biomass burning emissions.
Even though the total emissions are left unchanged, this change of distribution led to a significant decrease in the simulated burden and AOD of dust aerosols because the lifetime of super-coarse dust is shorter than for the other two bins as it is subject to a large sedimentation rate. Figure  5  shows the 2017 emissions of super-coarse dust. The highest emissions, at 0. 2-0. 3 kg m -1 yr -1 occur in the Gobi and Taklimakan deserts, which were impacted by severe dust storms in particular in May 2017. The Sahara, the Arabian Peninsula and parts of Iran and of Turkestan are also prominent. The emissions are very widespread in these regions, which is probably not realistic. Maps of probability of occurrence of observed AOD by MODIS dust AOD above different thresholds were computed using data kindly provided by Paul Ginoux (personal communication), which can serve as dust source functions.
An experimental version of IFS-AER that simulates a simplified dust mineralogy was used to compute a climatology of airborne calcite, using as an input the dataset of  Journet et al. (2014)  which provides an estimate of the calcite content in the clay and silt fraction of soils. Figure  6  shows the vertically integrated fraction of airborne calcite over coarse and super-coarse dust. The regional differences are great, especially for calcite emitted from clay surfaces as compared to coarse dust. A first order update parameterization is used to represent the uptake of HNO 3 over sea-salt and calcite particles. The rate constants of reactions (  8 ) and (  9 ) are computed in a simplified way as compared to the original scheme of Hauglustaine et al. (2014) for each sea-salt (SS) and desert-dust (DD) bin i: K 6 = 4πD 2 SSi N SSi D SSi 2D g + 4 νγ -1 (10)   2010 ), a dependence of the uptake coefficient on relative humidity is used.
(1997)  for sulfate and ammonium, from  Chin et al. (2002)  for BC, and from  Svenningsson et al. (2006)  for nitrate. For OM, the values are derived from Water Soluble Organic (WASO) of the OPAC (Optical Properties of Aerosols and Clouds) database  (Hess et al. , 1998)  4 Removal processes Removal processes consist of dry and wet deposition and sedimentation or gravitational settling. Wet deposition and sedimentation are similar to M09 but they are described again here for completeness.
the Airnow network which gathers observations mostly over the United States and Canada, and the AirBase, the European Air Quality Database operated by the European Environment Agency (EEA), which gathers observations over Europe. As metadata was not available for Airnow observations, data from all sites (1036 for PM 2. 5 and 354 for PM 10 in 2017) are used, which means that urban, suburban and rural stations are included in the scores. Similarly, the observations may be representative of background pollution or closer to traffic sources. For the Airbase observations, for which site information was available, it was chosen to focus on scores vs background rural stations (62 stations in all). Because of the relatively coarse horizontal resolution of the simulation (about 40 km), urban areas cannot be realistically represented and this will lead to a low bias as compared to observations, caused not from issues in the model or in emissions, but mostly from resolution.
Acknowledgements. This work is supported by the  Copernicus Atmospheric Monitoring Services (CAMS)  programme managed by ECMWF on behalf of the  European Commission. The authors would also like to thank  NASA , the  European Environment Agency , the  Airnow network  and the  United States Environmental Protection Agency (EPA)  for making the  MODIS, AirBase, Airnow  and CASTNET data publicly available.
Long term series On the wells with long term manual measurements, we applied the same modeling approach. The piezometric water head data are provided every ten days at the reference points (not at the measured well) while the oil levels were measured every two or three months. It can be seen that for this observation well (pz262) the fit is also correct. However, the sensitivity to the input parameters is small and thus the uncertainty in the parameter's value is high. This has two main reasons: the manual measurements are of worse quality than the automatic measurements, and there is no series of data on a short time scale to fit correctly the oil conductivity. Again the true thickness of the oil in the aquifer is difficult to determine with this approach. were acquired every two hours. In this area of the polluted site some layers of silt were identified, it is therefore possible that the oil movement in this well might be semi-confined or confined.
Sensitivity study of the obtained model parameters In the case of oil layers in unconfined aquifers, a sensitivity analysis was performed on the obtained fit parameters to estimate the relative role of each model parameter. The base values obtained from the best fit of the Pz262 data were used (case 0); the sensitivity of each model parameter was calculated by varying these base parameters by 10 % of the potential range for each parameter, called applied δ (Table  4 ). The potential range was estimated from the knowledge of the site characteristics which are presented in Table  4  as min and max. For the oil conductivities K1 and K2 the 10% range of variation were calculated on the decimal log of their base value, as classically done when using hydraulic conductivities. To quantify the individual sensitivity of each model parameter, we used the calculated vertical positions of the oil-water interface of Zow for different measuring periods.
Background: In the last 20 years, a general picture of the evolutionary relationships between geoemydid turtles (ca. 70 species distributed over the Northern hemisphere) has emerged from the analysis of molecular data. However, there is a paucity of good traditional morphological characters that correlate with the phylogeny, which are essential for the robust integration of fossil and molecular data. Part of this problem might be due to intrinsic limitations of traditional discrete characters. Here, we explore the use of continuous data in the form of 3D coordinates of homologous landmarks on the turtle shell for phylogenetic inference and the phylogenetic placement of single species on a scaffold molecular tree. We focus on the performance yielded by sampling the carapace and/or plastral lobes and using various phylogenetic methods.
Thus, the use of landmarks could help palliate the problem of the scarcity of discrete states. Geometric morphometric methods have been applied to geoemydids in studies of diversification, morphological convergence, and adaptation  (Claude et al. , 2003 (Claude et al. , , 2004;; McLaughlin & Stayton, 2016)  and shape asymmetry  (Rivera & Claude, 2008) , and also to validate character definitions  (Russell & Jamniczky, 2004 ). To our knowledge, the present study is the first attempt to use landmark data to reconstruct the phylogeny of geoemydids, or any other clade of turtles. We have two main objectives in our assessment of the performance of various phylogenetic reconstruction methods with our dataset. The first is to provide one more study of the performance of phylogenetic reconstruction with empirical morphometric data, which will contribute to understanding the behaviour and usefulness of the methods for future applications.