dataesr/scientific-paragraphs-categorization huggingface dataset:
The SMOS NRT SM product was evaluated with respect to the original SMOS Level 2 SM product using several months of data. The NRT SM product compares well with the L2 product. The most significant difference is that the NRT SM dataset shows local negative bias at the positions were the highest SM values were found (basically under tropical forest). The SMOS NRT SM product was also evaluated with respect to in situ measurements of SM with the SCAN and the USCRN. The NRT product shows similar performances to those of the L2 product. Actually, the mean and median correlation are slightly higher than those obtained for the L2 product. In addition, the STDD with respect to the in situ measurements is lower for the NRT product than for the L2 product. In summary, the SMOS NRT SM product shows similar performance to the Level 2 product but it has the advantage to be available in NRT.
NRT brightness temperatures are received by ECMWF from ESA in less than 3 h after sensing. The NRT SM production takes on average of 15 min (the arrival of new NRT T b data is checked every 30 min and the actual NRT SM production takes a few minutes). The SMOS NRT SM product is delivered to ESA and EUMETSAT for dissemination via EUMETCast. Therefore, the SMOS NRT SM data are available for a large range of operational applications such as numerical weather prediction, hydrological forecasting and crop modelling. Data availability. The datasets used in this study (Sect. 2) are publicly available. The SMOS L2 SM and NRT SM data can be downloaded from the ESA. The SMOS NRT SM data are also available via EUMETCast in NRT. The in situ measurements can be downloaded from the International Soil Moisture Network  (Dorigo et al.
and minimum values of the T b in the local time (λ, φ) series, SM T min b and SM T max b are the associated SM in the SMOS Level 2 SM reference dataset. The index I is computed for each incidenceangle bin and polarization at the time t of the SMOS acquisition and it contains local information on the dynamic ranges of both the measured T b and the reference SM. In the current version of the processor (v100), T max,min b and SM T max,min b.
Acknowledgements. The authors acknowledge useful discussions with Filipe Aires and  Catherine Prigent  on neural network algorithms to retrieve soil moisture from microwave observations. The authors acknowledge interesting and constructive comments from two anonymous referees and  W. Wagner  that improved the manuscript, in particular regarding the comparison to the NRT ASCAT processing chain. Edited by:  Gerrit H. de Rooij  Reviewed by:  Wolfgang Wagner  and two anonymous referees.
In addition, the basis of the shape of this occlusal enamel pattern was found to support a reliable assignment of the various source populations  [45] , which pleads for further investigations aimed at reconstructing the history of horse breeding. Here, we assess the relevance of occlusal enamel patterns in the lower cheek teeth of extant Old World equids, collected to provide reliable interspecific taxonomic and phylogenetic signals. A phylogenetic signal can be observed when related species show close phenotypic relationships  [46] [47] [48]. However, to test this signal we have chosen to map the underlying morphospace onto the whole-exome based phylogeny presented by Jónsson and co-workers  [5] , following previous approaches for the projection of a phylogenetic tree into geometric morphometric space  [49, 50] , and estimate the strength of the phylogenetic signal in the morphometric dataset using multivariate K statistic  [51].
E. grevyi Grévy's zebra GRV the superimposition process, they are slid along the tangent of the curves using the bending energy algorithm  [55]  to minimize the difference between the shape configurations  [56]. The by-product of this superimposition is a new set of Procrustes coordinates (or shape coordinates) used for statistical analyses. The GPS was performed with a TPSrelw v. 1. 54  [57]. We provide the full morphometric dataset as the electronic supplementary material, including specimen ID, grouping factors and Procrustes coordinates (electronic supplementary material, S2). [15]  and (c) their common GMM protocol for the quantification of the enamel folding with the location of the 8 landmarks (dots with grey filling) and the number of semilandmarks (table) sampling the curves of the enamel folds between each landmarks.
This approach calculates the most discriminant axes of variation among the taxonomic grouping factors, following normalization of the intra-group variance by creating a discriminant shape space. Note that the latter is different from the original shape space obtained from a principal component analysis (PCA) which calculates the main axis of variation in the shape coordinates without normalization. To estimate the performance of the anatomical and taxonomic discriminant models in practice, and their ability to identify the right anatomical position or the right taxonomic group of an isolated tooth, we calculated the percentage of correct classification of each specimen into its known group, be it anatomical or taxonomic, following a twofold cross-validation approach. This consisted of partitioning the dataset into two subsets: the training set on which the analysis was performed, and the testing set that was used to validate the analysis.
This procedure is done over multiple rounds (10 000) of cross-validation, changing the dataset partitioning at every round. This provided an average of correct classification for each specimen and a percentage of correct classification, or correct crossvalidation (CCV). To reduce the dimensionality of our shape dataset, we performed a PCA on the shape coordinates before each CVA analysis, and retained the PC scores that explained 99% of the total shape variance. To visualize shape changes along the main axis of variation (PCA) or along the discriminant axes (CVA), we used a multivariate regression approach  [58]. All these analyses were performed with R  [54] , using the libraries ade4  [59]  and geomorph  [60]. Graphical displays were performed with MorphoJ  [61].
Phylogenetic signal in enamel outline shape We used the published molecular phylogeny of equids (figure  3 ; electronic supplementary material, S3) built for 10 taxa: two horses (E. ferus przewalskii, E. f. caballus), four zebras (E. quagga quagga, E. q. burchellii, E. grevyi, E. zebra hartmannae), two asses (E. africanus somaliensis, E. a. asinus) and two hemiones (E. h. onager, E. kiang). This phylogeny was obtained applying a maximum-likelihood method on a super-matrix of 20 374 protein-coding genes, each partitioned in 1st + 2nd and 3rd codon position (representing a total of 31 219 584 sites). Node robustness was assessed using 100 bootstrap pseudoreplicates. The analysis was performed using the phylogenetic module of the automated PALEOMIX pipeline  [62]. To assess the association between the molecular phylogeny and the dental shape space, we created a phylomorphospace which fitted the shape changes of each tooth from the 10 species onto the tree topology of the molecular phylogeny (including branch length)  [49, 50].
It is then possible to graphically visualize the evolutionary changes of the teeth shape from the common ancestor of the Equus species, and each internal node and terminal tip of the phylogeny. To assess the phylogenetic signal in the shape space of the four teeth, we used the Kmult method  [51] ; which is more suited to high-dimensional multivariate data and less affected by the difference of traits variation among species  [65]. This method is a multivariate generalization of the K statistic which estimates the strength of the phylogenetic signal in a univariate trait relative to a Brownian motion model of evolution  [65]. Under a Brownian motion model of evolution the expected Kmult value is 1. 0. Observed Kmult values are evaluated by comparison with the permutated K value from randomized shape data relative to the tree topology of the molecular phylogeny.
Anatomical distinction of equine mandibular cheek teeth excluding P/2 and M/3 The discriminant model (figure  4 ) found correct anatomical identification for each mandibular tooth across the 15 taxa dataset for 93% of the classifications after cross-validation. This shows that this model can accurately identify the anatomical position of any isolated mandibular tooth of the genus Equus from the archaeological record with a false-positive probability of only 7%. Misclassifications were systematically between M/1 and M/2 (37%) and between P/3 and P/4 (49%). The remaining 14% were random. The visualization of the shape changes along canonical axes 1 and 2 (figure  4 ) showed that the main differences between premolars and molars were a more penetrating ectoflexid, and consequently, a less wide postflexid in the latter.
Taxonomic distinction among equids The discriminant model of the CVA shows that all four mandibular teeth could distinguish the 15 equine taxa of our dataset (table 1), with more than 95% of correct classification with cross-validation (table  3 ). Premolars performed better than molars, with P/3 being the most efficient with a 100% cross-validated classification (CCV). The pooled within-group shape space of the 15 taxa studied (figure  5 ) shows a clear taxonomic signal separating the three clades of zebras, asses and horses. However, the hemippe, also known as Morphospace for the four mandibular equine cheek teeth. The shape changes of the enamel folding along the major axes of variations (PC1 and PC2) are displayed for the extreme values of each axis. The colour of the dots corresponds to the molecular clades (blue: caballine horses, red: asses, orange: hemiones, green: zebras) and hybrids (grey filling).
But the latest study on the speciation process in Equus is based on an extensive genome-wide dataset and has provided strong evidence for a speciation gene flow in equids  [10]. These authors found four, yet undated, events of gene flow among equine lineages following their divergence: between horses and zebra/ass clades, between kiang and donkey, between the African wild ass and Grévy's zebra, and between asses and mountain zebra. This clearly supports gene flow as one of the factors explaining the incomplete hierarchical phylogenetic relationships in Equus dental shape variation.
Acknowledgements. This work would not have been possible without the generous help of  Dr Véra Eisenmann , who authorized the use of her photographic documentation and offered insightful comments on the initial steps of this study. We thank  Shiva Sheikhi  ( UMR 7209 ) for the scanning of this documentation. We also thank  J. Lesur , curator of the Mammalian osteological collections at the  National Museum of Natural History of Paris (MNHN)  for access to the equid collections. We are very grateful to  David Polly  and three anonymous reviewers for their constructive critiques that have greatly improved the quality of the manuscript.
Noise and air pollution indicators were chosen as good indicators of environmental pollution in the LMA. Indeed, in the Lyon Metropolitan Area, traffic road has been recognized to be one of the main contributors of air pollution (http://www. ppa-lyon. org/). Noise exposure constitutes additional environmental consequences of the traffic road. In addition, following the recommendation of the second plan of environmental health of the Rhône-Alpes region, a web platform named Orphane (http://www. orhane. fr/) has been created to identify the part of the region accumulating high levels of air pollution and noise to better target action and decision making. -Next, 2500 phone numbers per category were randomly selected from the France Telecom Institute and other phone companies-creating a random data base of 10,000 phone numbers.
Heavy Residential Traffic Exposure Heavy residential traffic exposure was measured by computing the percentage of inhabitants in each census block living within a specified distance from the road  [35, 36]. The local air quality monitoring network (Atmo Air Rhône-Alpes) provided the traffic density data and a spatial dataset for roads and highways in the Lyon Metropolitan Area in 2005. More precisely, this dataset included Annual Average Daily Traffic (AADT) as an attribute describing traffic volume (provided by the Greater Lyon Urban Community) for each road (the number of vehicles per 24-h period on all roads in the study area). In our study, major roads were defined as those that had an AADT higher than 5000 vehicles per day. Using ArcGis software, the residential traffic exposure indicator for each census block was calculated following the two steps: (i) First, we modeled the distribution of inhabitants in buildings.
The total number of inhabitants living in a given census block was distributed in each residential building located within the census block. To do this, two data sources were used: building volume according to the topographic database (from the National Geographic Institute) and the number of inhabitants per census block according to the national census, collected by the French National Statistics Institute (INSEE). The number of inhabitants per residential building was calculated assuming the equi-distribution to its volume. (ii) Second, the percentage of inhabitants living within 150 m of a major road was calculated for each census block. More precisely, circles with radii of 150 m were created around each road segment, as shown in Figure  2. The widest radius, of 150 m, was chosen based on studies revealing that NO 2 concentrations drop off very rapidly within the first 50 m, and are divided by ten after 100 m  [37, 38].
We intersected these circles (buffers) with the topological database to pinpoint residential buildings located inside each circle which would be considered exposed to high-traffic roads. The total number of exposed inhabitants per census block was obtained by adding together the number of inhabitants of all the residential buildings within the buffers. Finally, the residential traffic exposure indicator was estimated at census block level by dividing the total number of exposed inhabitants by the population of the census block. in the study area). In our study, major roads were defined as those that had an AADT higher than 5000 vehicles per day. Using ArcGis software, the residential traffic exposure indicator for each census block was calculated following the two steps: (i) First, we modeled the distribution of inhabitants in buildings.
The total number of inhabitants living in a given census block was distributed in each residential building located within the census block. To do this, two data sources were used: building volume according to the topographic database (from the National Geographic Institute) and the number of inhabitants per census block according to the national census, collected by the French National Statistics Institute (INSEE). The number of inhabitants per residential building was calculated assuming the equi-distribution to its volume. (ii) Second, the percentage of inhabitants living within 150 m of a major road was calculated for each census block. More precisely, circles with radii of 150 m were created around each road segment, as shown in Figure  2. The widest radius, of 150 m, was chosen based on studies revealing that NO2 concentrations drop off very rapidly within the first 50 m, and are divided by ten after 100 m  [37, 38].
Noise Exposure Residential noise exposure includes related noise coming from all sources: road traffic, aircraft traffic, industry and railway. Noise pollution was measured in 2007, in accordance with the European Environmental Noise Directive (END, 2002/49/EC) [39], using GIpSynoise software and various input data including information on road and railway characteristics, topography, meteorological factors and other data on sound reflection and diffraction. Acoustic modeling of noise levels with a spatial resolution of 10 × 10 m at 4 m above ground level, across the Lyon metropolitan area was performed by the Greater Lyon Urban Community (the political body comprising the municipalities of the Lyon metropolitan area). In our study, the metric used to characterize noise in each census block is a population-weighted average noise exposure which combines the European Lden indicator (day-evening-night level, expressed in decibels noted dB) and the estimated population living in each residential building.
Green Spaces Spatial land cover datasets for the Lyon Metropolitan area were processed using ArcMap GIS software (Redlands, CA, USA) in order to produce a green space index (Figure  5 ). In our study, all the different types of natural areas (e. g. , parks, forests) were included. More precisely, our greenness index was the percentage of green spaces (km 2 ) in the total area of the census block. (More methodological details in  [43] ).
Then, MCA and HC were conducted separately on these 3 groups. Each category resulting from the HC was characterized by the statistically significant supplementary variables Analyses were performed with R software (R-version 3. 2. 2). The MCA and HC were performed using the 'factoMineR' package for R. Bi-variate analysis realized in our study included two different statistical tests: the mean-comparison test and the non-parametric test for trend across ordered groups; the level of significance was α = 5%.
Previous studies reported a significant association between perceived air quality and self-reported health status  [50, 51] : perceived health risk related to air pollution was found to be associated with the level of education and occupational status. Regarding gender, some authors have argued that women, in particular those with young children, tend to be more aware of, or more anxious about, environmental risks, and suggest that women are more environmentally aware than men  [28]. Other authors have suggested that women tend to have a better sense of smell than men  [52]. There are several limitations to our study. First, the homogenous profiles were drawn up and analyzed using individual data collected from a telephone survey, which inevitably introduced a selection bias in the study population.
37. Soetaert, K. , Meersche, K. V. D. , and Oevelen, D. V (2009). Package limSolve, solving linear inverse models in R. with mean values shown as horizontal lines. Mean total methane production was greater for mixtures than for individual communities (t-test: P < 0. 001 in 9 cases), except when measured against community P13 (the best performer). B) NMDS plot of unweighted unifrac of 10 mixtures (grey) and 9 individual communities (white). Numbers in circles refer to individual community identifiers (Table  1 ). Community P13 was significantly closer in composition to the 10 mixed communities than any other community (weighted and unweighted UniFrac distances; Paired t-tests; P < 0. 001, in all cases). There was also a significant link between the community composition and the difference in gas production between the communities (see Figure  S1 ).
Analyses of sequenced samples MiSeq amplicon reads were merged using Illumina-utils software  [31]. We quality-filtered only the mismatches in the overlapping region between read pairs using a minimum overlap (--min-overlap-size) of 200 nt and a minimum quality Phred score (--min-qual-score) of Q20. We allowed no more than five mismatches per 100 nt (-P 0. 05) over the 200 nt overlapping region. Reads that fulfilled the quality criteria were analysed using Quantitative Insights Into Microbial Ecology (QIIME v. 1. 7) [32]. We removed chimera using the identify_chimeric_seqs. py script, UCHIME reference 'Gold' database and USEARCH  [33, 34] , which we also used to select OTUs. We assigned the taxonomy of our reads with QIIME pick_open_reference_otus. py function, using the Greengenes database version v13_8  [35]  as a reference with a minimum cluster size of 2 (i.
QUANTIFICATION AND STATISTICAL ANALYSIS MacQiime was used to calculate alpha and beta diversity data and produce NMDS plots. Data obtained with MacQiime was later combined with the gas production data and analysed using JMP Pro 13 software (SAP) as described in the Figure legends. For the NNLS analysis, following removal of low abundance OTUs and cumulative sum scaling transformation, the resulting biom file was used to create a matrix  ∈ ℤ ≥0  ×  (m rows of OTUs by n sample columns) for all of the single communities, and a column vector  ∈ ℤ ≥0  for each of the mixed communities; both  and  hold non-negative integers of OTU abundances. Note that one of the individual samples contained a negligible number of reads and was discarded from the analysis. The contribution, or weight, of each seed sample to the pattern of OTUs observed in each of the mixed communities is given by the column vector  ∈ ℝ  when solving a system of linear equations  =.
DATA AND SOFTWARE AVAILABILITY The raw sequences obtained from our experiments are available at the European Nucleotide Archive and may be accessed at http://www. ebi. ac. uk/ena/data/view/PRJEB21193 (Experiment 1) and http://www. ebi. ac. uk/ena/data/view/PRJEB21187 (Experiment 2). We also included the R code that allows the user to calculate the contribution a single community has in a mix of communities (see Method S1). Using this code, a NNLS analysis can be conducted with the input of a pre-filtered OTU table.
This annotated code can be run using R in order to obtain the contribution of individual communities towards the community mix. The input files needed are the. biom file containing the compositions of communities analysed and their phylogeny, preferably prepared using Cumulative Sum Scaling normalisation (see STAR methods). It also requires a mapping file as described in http://qiime. org/documentation/file_formats. html. The version of the code is suited for analysis of our dataset but can be readily adapted for any dataset containing multiple communities amplicon data.
G8270-1KG A B Figure 1 The script was run in RStudio v1. 0. 143 using R version  3. 4. 0 (2017-04-21)  and the following R package versions: limSolve_1. 5. 5. 2, nnls_1. 4, biomformat_1. 4. 0, Phyloseq_1. 20. 0.
Acknowledgements JDN is thankful for his CSIC PhD grant  JAEPre09-01155 , which also covered his research stay at  Rice University. WKM and AMB acknowledge nancial support from  Spanish MINECO  through project grants  ENE2013-48816-C5-5-R ,  ENE2016-79282-C5-1-R  (including associated European Regional Development Funds), and the  Gobierno de Aragón  under project  DGA-T66  (including associated European Social Funds). RA gratefully acknowledges the support from  MINECO  through project grants  FIS2013-46159-C3-3-P  and  MAT2016-79776-P. Some  of the research has received funding from the  European Union   FP7 program  under Grant Agreement  312483 ESTEEM2  ( Integrated Infrastructure Initiative-I3 ), and from the  European Union   H2020 program  under the grant  696656   Graphene Flagship.
10 rface-tracked satellite drifters, Argo floats, autonomous ocean gliders, and satellite remote sensing continue to revolutionise our ability to measure oceanic change. However, the features of the Greater Agulhas Current System are extremely energetic, narrow and deep. Satellite altimeter data are poor close to the coast, alias sub-seasonal signals,  11  and cannot resolve high-velocity shears. Drifters and Argo floats are quickly flushed out of the system into more quiescent waters, and gliders are challenging to control in currents that are ten times faster than their gliding speed. Hence, there is a critical need for long-term sustained measurements from moored instrumental arrays and repeat-hydrographic observations from ships to be able to study the Greater Agulhas Current System to its full depth and resolve subsequent changes over time.