dataesr/scientific-paragraphs-categorization huggingface dataset:
The measure of predictive discrimination used to characterize model performance, in both the original sample and the validation samples, was the area under the receiver operating characteristic curve (AUC). Average optimism, as the difference between bootstrap performance and test performance, was calculated. Optimism-corrected performance was then estimate by subtraction of the average optimism from the apparent performance (initial multivariate model). SAS software version 9. 4 was used for validation. Initial multivariate model had an AUC of 0. 78. In the 200 bootstrap samples, the mean AUC was 0. 80. When the models from each bootstrap sample were tested in the original sample, the AUC decreased to 0. 75. The optimism was then equal to 0. 05 and the optimism-corrected AUC equal to 0. 73.
(b) Experimental design Our study is based on a recently published dataset that estimated male fitness through marker-assisted paternity analyses in two common gardens  [21]. Seeds were collected from 35 populations located in northern Spain that were bulked and grown for three generations in a common garden in Lausanne. Male and female fitness components were assessed after mating in two common gardens at varying densities and equal sex-ratios in Montpellier. A peculiarity of the design is that males and females were grown in pairs, allowing us to compare male strategies that were successful at siring ovules locally versus over longer distances. In each garden, female -male pairs were transplanted into pots that were assigned randomly to a position in a 10 Â 10 grid. Pots in both gardens were initially established at a low density of 1.
Note that our design does not allow a statistical comparison between densities; we explored the effect of density specifically by means of computer simulations (see below). In both gardens, leaves of all adults were sampled at the end of the experiment and preserved in silica gel for later DNA extraction and genotyping. All seeds of all 100 females were harvested in both gardens by drying vegetative parts, threshing and winnowing seeds from the samples. Seeds were then counted for each female using an automatic seed counter (Elmor C3; Elmor Angewandte Elektronik, Schwyz, Switzerland). On each male, inflorescences were harvested, dried and weighed to estimate inflorescence weight, which is known to provide a reliable estimate of pollen production  [37]. To characterize male dispersal abilities, we extracted individual mean dispersal distances of pollen from previous inferences  [21] , in which genotype and spatial data were used in a spatially explicit model of pollen dispersal kernels with a negative exponential power function.
We further calculated and plotted 95% confidence intervals for all components of reproductive success (I-XI) and compared the confidence intervals between sexes. We used this bootstrap resampling to assess the significance of covariance between components of male fitness by computing the p-value associated with a null covariance in the bootstrap distribution. Finally, we performed bivariate linear regressions for all our fitness components against both pollen dispersal distance and weight to quantify the variance for each fitness component explained by these two traits. All statistical analyses were performed using the lm, glm and glmer functions in the lme4 package  [44]  in R v. 3. 2. 2 [45].
002  (Sugita et al. 2019). Two instruments, the optical navigation camera (ONC-T) and the near-infrared spectrometer (NIRS3) are devoted to investigating the surface composition variations mapping the complete surface of Ryugu  (Kameda et al. 2017; Suzuki et al. 2018; Iwata et al. 2017). The asteroid shows a surface rich in geological structures (craters, boulders of different size, texture, ridges, depressions, troughs, etc. ). The largest boulder, Otohime Saxum, located near the south pole has a dimension of about 160 m  (Sugita et al. 2019). The telescopic ONC-T obtained spectrophotometric data by imaging Ryugu with seven broadband filters spanning from 398 to 946 nm at different surface resolutions. NIRS3 acquired reflectance spectra in the wavelength range from 1800 to 3200 nm operating in scanning mode  (Kitazato et al.
2019). The spectral data show a weak positive spectral slope in the first part of the spectra and a clear weak narrow absorption band at 2720 nm, detected across the entire observed surface. The absorption band indicates the presence of OH-bearing minerals all over the surface, associated to Mg-rich phyllosilicates  (Kitazato et al. 2019). No meteoritic samples seem to have reflectance spectra to perfectly match those of Ryugu's surface that were obtained at the same wavelengths. Possible similarities have been found with the spectra of thermally-metamorphosed CI chondrites and shocked CM chondrites. A&A 629, A13 (2019)  The spectral and spectrophotometric data show an almost homogeneous surface with small variations  (Kitazato et al. 2019; Sugita et al. 2019 ). In the present work we used a multivariate statistical method to separately analyze the ONC-T broadband filters and the NIRS3 spectral data in order to characterize possible spectrophotometric and spectroscopic heterogeneities of the Ryugu surface and to compare them with morphological surface structures.
Data We used the ONC-T data obtained on July 12, 2018 at 20 km altitude, which have almost global coverage at a resolution of 2 m pixel -1. We used the normalized reflectance measured in the seven broadband filters (ul = 397. 5 nm; b = 479. 8 nm; v = 548. 9 nm, n = 589. 9 nm; w = 700. 1 nm; x = 857. 3 nm; and p = 945. 1 nm) with calibration and errors as reported by  Tatsumi et al. (2019). The data have been coregistered as described by  Honda et al. (2018 Honda et al. ( , 2019) )  and photometrically corrected following the Hapke photometric model obtained for ONC-T v-filter images combined with ground-based observations as described by  Sugita et al. (2019). Since the model parameters for the other band filters are still under examination  (Domingue et al. 2019) , the other filter images are also calibrated using this single photometric model.
The data with emission and incidence angles larger than 50 • have been eliminated to avoid shadow effects. Then the mean value of a 5 × 5 pixel area (about 10 × 10 m) is calculated to reduce noise. The ONC-T data were also analyzed by  Honda et al. (2019)  using different clustering methods. For the NIRS3 data, we used the spectra collected on July 11 and July 19, 2018 which allowed us to obtain near-global coverage at a spatial resolution of 40 and 20 m px -1 , respectively. We also analyzed the spectral data of October 30, 2018, which cover the equatorial ridge of the asteroid Ryugu at a higher resolution of about 10 m px -1. These observations were carried out at phase angles of 18. 2 • , 17. 5-17. 8 • , and 7. 6-7. 8 • respectively. The NIRS3 data were thermally and photometrically corrected as described in  Kitazato et al.
The G-mode multivariate statistical method In order to investigate the compositional surface variation of the asteroid Ryugu, we applied the G-mode multivariate statistical analysis  (Coradini et al. 1977; Gavrishin et al. 1992)  to the data described above. The G-mode method has been widely used in the planetary field to classify asteroids  (Barucci et al. 1987; Fulchignoni et al. 2000; Hasselmann et al. 2013) , transneptunian objects  (Barucci et al. 2005; Belskaya et al. 2015) , and to search for variations in the surface composition of the asteroid Steins  (Leyrat et al. 2010 ) and on the comet 67P  (Perna et al. 2017 ) in order to analyze the images taken with different filters by the Rosetta's OSIRIS camera  (Keller et al. 2007). This method allows the user to obtain an automatic statistical clustering of a sample containing N objects (in this work the pixels) described by M variables (the normalized reflectance of each filter for the ONC-T data and of a selected set of wavelength channels for the NIRS3 data) in terms of homogeneous groups without any a priori criteria and taking into account the instrumental errors in measuring each variable.
NIRS3 data analysis The analysis of NIRS3 data also confirms general spectral homogeneity on the Ryugu surface. For our analysis we included 24 variables (wavelengths reported in the first column of Table  2 ), selected between those more significative for slope and band variations, eliminating the wavelengths with higher uncertainties. The analysis of the data of July 11 (8300 spectra) and those of July 19 (13 385 spectra) at 3σ confidence level gave the same results with two groups (Figs. 3a  and c ), which are distinguishable essentially in the spectral slope between 2200 and 2600 nm and slightly in the 2720 nm band depth. The first group concerns the majority of the measured surface (about 97%) and the second one (which is much smaller) contains less than 3% of the obtained spectra. The average spectrum of the smaller group is redder than that of the larger group (see the mean values reported for each variable in Table  2 for 3σ ).
4  (a and b, respectively). As the results of the analysis of July 11 and July 19 are almost the same, we reported in Table  2  only the mean values of each group obtained with the analysis of July 19 data. The spectra acquired on October 30 were collected closer to the asteroid, and constitute a data set (16 528 spectra) with a higher resolution (about 10 m px -1 ) with respect to those of July 11 and 19. This implies much smaller coverage of the asteroid surface concentrate along the equator between 20 • and -40 • latitudes. The analysis of this data set at the 3σ level always shows the separation of the sample in the same two groups found A13, page 4 of 10  Notes. The number of sample for each group is also reported. with the other data sets at the same confidence level. The larger group contains most of the objects, and the smallest group (<2%) is redder than the larger one.
Discussion The results that we obtained here using G-mode multivariate statistics at high confidence level (3σ) confirm general homogeneity of the surface composition. At this confidence level and using data obtained with ONC-T and NIRS3, only two classes are established with both instruments: one large and spread over about 97% of the observed surface and one much smaller. In the ONC-T data, the small group has a more flat/bluish spectrum than the large one (which is more red) and this can be associated with fresher material  (Sugita et al. 2019 ) compared to the rest of the surface, which is more altered due to space weathering. In the NIRS3 data the small group which appears at 3σ has more reddish spectra than the big group average spectrum. As the effect of space weathering on the wavelength range of 2000-3000 nm is less important than in the visible  (Lantz et al.
In the near-infrared wavelength for the NIRS3 data analysis, the surface of Ryugu is less covered and the data are acquired at lower spatial resolution than the visible wavelength by ONC-T data. The differences among the obtained five groups (Figs. 3b  and d ) are associated essentially with the differences in slopes between 2000 and 2600 nm (Fig. 4a ) and in the areas of the 2720 nm bands (Fig. 4b ). The reddest spectral groups (#2, #3, and #5 in Figs. 3b  and d ) represent some areas with large boulders and areas inside a few specific craters like Momotaro, Kibidango, Urashima, and Cendrillon. These red slopes could be attributed to the presence of small sized regolith grains. From laboratory experiments on carbonaceous chondrite meteorites, the slope of the spectra changes in function of the grain sizes.
Conclusions The combined analysis of both ONC-T and NIRS3 data allows us to characterize spectral properties of the major morphological surface features. We analyzed these data with the G-mode multivariate statistical analysis to detect very small surface variations. The statistical analysis of the spectrophotometry by ONC-T and NIRS3 spectral data at 3σ confidence level clearly highlights a very homogeneous surface with slightly different small areas containing about 3% of the analyzed data. Decreasing the confidence level to 2σ, few small groups are detected with different average spectral slopes. The different groups obtained from NIRS3 spectral data show also differences in the area of the 2720 nm band that could be attributed to different content of hydrated phyllosilicates, nevertheless grain size and porosity are also important parameters that could change not only the spectral slopes but also the band depth.
The areas that are supposed to be younger from the ONC-T analysis do not appear in the NIRS3 data analysis as the effect of space weathering is less important in the near-infrared wavelengths and the spatial resolution is lower. We suggest that the groups having redder spectra slopes, could also refer to surface zones with small sized regolith grains. Moreover, the obtained results show a clear spectral dichotomy both between the eastern and western hemispheres and between the northern and southern hemispheres. From our results, the parts of surface represented in the maps with orange and red colors and localized on the southwestern hemisphere could be connected to the presence of small sized regolith grains. The former dichotomy is also discussed by  Sugita et al. (2019)  on the basis of the image geomorphological interpretation.
The surface spectral variations we found here can give constrains on the re-accumulation phase of the present rubble-pile body after the catastrophic disruption of the parent asteroid  (Michel et al. 2015). Hayabusa2 is still working and some specific areas will have very detailed data both with ONC-T and NIRS3 instruments. We will also have imaging data of the sampling sites at very high resolution and more detailed analysis putting together available data of all the instruments will allow better interpretation of Ryugu's surface characteristics.
Acknowledgements. We thank the  Haybusa2 JAXA team  for their efforts in making the mission successful. Hayabusa2 was developed and built under the leadership of JAXA, with contributions from the  DLR  and the  CNES , and in collaboration with  NASA ,  Nagoya University, University of Tokyo ,  National Astronomical Observatory of Japan, Aizu University, Kobe University , and other universities, institutes, and companies in Japan. We would also wish to thank all the engineers who contributed to the success of the Hayabusa2 mission, especially  T. Masuda ,  S. Yasuda ,  K. Matsushima , and  T. Ohshima. This study was supported by  JSPS International Planetary Network. M. A. B. , P. H. , J. D. P. D. , and M. F. acknowledge funding support from the  CNES. D. P. has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie actions  (grant agreement no.
The number of measurement stations were for each year: 2002: 4, 2005: 9, 2008: 15, 2011: 14 and 2014: 18, with fewer entries for each of the industrial (I), urban (U) and rural (R) sub-selections (Appendix A). The pollution data from the ICP stations are reported to the ICP-materials data base as monthly average values obtained with different measurements techniques (continuous monitoring or passive sampling) with different initial resolution  [9]. The minimum, 10 th percentile, average, 90 th percentile and maximum concentration values for the annual station data (in µg/m 3 ), from year 2002 to 2014, were for SO 2 (0. 1, 0. 5, 5, 14, 39), for NO 2 (0. 6, 1. 9, 23, 46, 67) and for PM 10 (3. 8, 7, 24, 48, 62). The trends for the pollution values measured at the ICP-materials stations since 1987 were strongly negative for SO 2 and clearly negative for NO 2 until about year 2000.
Results The results are presented below, of the cleaning cost and possible savings for sheltered white painted steel facades and modern glass, for the measured air pollution situation and when all the impacting air pollutants were, hypothetically, reduced by 50%, according to Equations (  1 ) to (3). The results for modern glass are presented as the averages calculated through Equations (  2 ) and (3). The separate results for Equations (  2 ) and (  3 ) are shown in Appendix B, Figures  A1-A3. The costs were calculated from Equations (  4 ) and (  5 ) for the averages of all the ICP-materials stations, and for the sub-selections of stations noted as urban (U), industrial (I) and rural (R) (Figures  4 5 6 ) and Oslo (Figure  7 ), where the annual average measured values for the needed environmental data were available in the ICP-materials database (Appendix A).
Acknowledgments: The basis for this work is the 30 years of field work, analysis and reporting by the partners in the International Co-operative Programme on Effects on Materials, including Historic and Cultural Monuments (ICP-materials). The ICP-materials programme was started in 1985 to provide a scientific basis for protocols and regulations to be developed within the  Convention on Long-range Transboundary Air Pollution  under the  United Nations Economic Commission for Europe (UNECE). We thank all present and past ICP-materials partners for their invaluable work over all these years, and for the particle and soiling measurements, which started around year 2000, and which today make works like this possible.
e. EPG) was not significant for Plagiorchis sp. (ρ = 0. 18, P = 0. 19), whereas it was weak for both E. caproni (ρ = 0. 51, P = 0. 053) and S. mansoni (when considering the number of schistosome pairs ρ = 0. 43, P = 0. 038; when considering the number of schistosome individuals ρ = 0. 46, P = 0. 058). Comparisons for Anchitrema sp. could not be made given the small number of infections observed. Similarly, the eight faecal specimens collected from underneath the wire-mesh live trap were not sufficient for meaningful statistical comparisons; however, sensitivity of the Mini-FLOTAC technique was identical to the results obtained on rectal faecal samples collected from the same individual (Table  3 ). For all trematode species, representative specimens were archived at the Natural History Museum (London, UK) under the accession numbers 2018.
3. 7. 33-38 (E. caproni), 2018. 3. 7. 39-67 (Plagiorchis sp. ), and 2019. 2. 13. 1-3 (Anchitrema sp. ). Schistosome trematodes were stored in the Schistosomiasis Collection at the Natural History Museum (SCAN)  [30]. Sequencing data were deposited in the GenBank database for Schistosoma spp. (accession numbers MF776585-97 for ITS and MF919405-28 for cox1)  [13] , Plagiorchis sp. (accession numbers MH633855-62 for ITS and MH673675-82 Table  2  Percent prevalence (95% confidence intervals in parentheses) and intensity (median and range in parentheses) of trematode infections in Hubert's multimammate mice (Mastomys huberti) at post-mortem examination (infection intensity expressed as parasite counts) and Mini-FLOTAC analysis (infection intensity expressed as eggs per gram) a One pair was formed of S.
Acknowledgements This study would have not been possible without the support of the Senegalese communities involved in the project, and in particular without the commitment by  Boubacar Bâ ,  Alassane Ndiaye , and  Cheikh Thiam  and his family. We would also like to thank  Rodney Bray  and  Vasyl Tkach  for their outstanding support with the identification of trematode taxa, and acknowledge the immense contribution made by all the wild rodents trapped and sacrificed during this study.
Development of the large SNPs resource and characteristics of the low-density SNP array After applying filters, we identified a RAD data set of 12, 204 sequences each containing one or two SNPs that met our specifications and that are made readily available to the scientific community (see Additional file 1: S1). The number of RADs per linkage groups (LGs) varies between 137 (LG 33) and 563 (LG 6). These RAD tags are spaced by 0. 119 cM (+/-0. 039) in average. They were found in all LGs and they cover the linkage map relatively evenly, although there were large gaps on LGs 11 (top), 12 (top and bottom), 27 (bottom), 32 (top), 33 (bottom), 37 (bottom), and 39 (top) (Fig. 1a ). One estimate of the local recombination rate for each SNP (i. e. one index of the relative power of markers for LD-based mapping approaches) is provided in Additional file 1: S1 and S2.
Efficiency of the low-density SNPs array Among the 30 replaced SNPs for the empirical test in the Taurion River, 10 failed at the amplification step (see Additional file 1: S2). This resulted in a set of 182 markers that successfully amplified, although only 92 of them were polymorphic in this river (among which only one mitochondrial marker of the 10 INRA and mitochondrial added markers). After removing individuals with more than a third of missing data, and keeping only individuals for which we had both SNPs and microsatellite data, our final dataset comprised 197 individuals (167 individuals from the Taurion basin and 30 from the Soueich trout farm). Expected heterozygosity ranged from 0. 18 to 0. 22 for SNPs and from 0. 58 to 0. 73 for microsatellites. Fst ranged from 0. 022 to 0. 070 for SNPs and from 0.
These SNPs were anchored to the-high density S. trutta linkage map using an intermediate step of physical mapping to the Atlantic salmon reference genome: using their relative positions on the Atlantic salmon reference genome, it was possible to determine the relative mapping positions of a large number of additional RAD loci that were not present on the brown trout linkage map  [59]. We applied a series of filters which allowed for selecting a panel of SNP markers that were (i) likely to be highly polymorphic, (ii) mapped on the linkage map, and (iii) present in the two brown trout lineages. To do so, we removed from the initial database all SNPs with a minimum allele frequency (MAF) of 5% or less using vcftools  [86]  based on all individuals (AT + ME). Then, in order to have clean sequences, straightforward to be genotyped, RAD sequences with more than two SNPs, and SNP positions falling in the first or last 30 bp of the RAD sequences were also removed, as well as sequences with undetermined (N) nucleotides.
Fin samples were sent to the LGC Genomics Company for DNA extraction and for multilocus genotyping to 192 SNPs markers using the KAS-PAR®  [87]. Note that 30 SNP markers from the initial 192 SNP panel did not amplify (see the Results), and were hence replaced by 30 other SNPs from the 245 SNPs filtered (see methods) to improve the SNP array (see Additional file 1: S2 for details). Additionally, all individuals were genotyped at thirteen microsatellites assembled in PCR multiplexes (see Additional file 1: S7, and Saint-Pé et al. , 2018 for details). We tested whether the selected SNPs were likely influenced by selection using the Fst outlier detection method implemented in the fsthet R package  [95] , in which outlier values of F ST can be identified in a plot of F ST vs. heterozygosity  [72].
From both SNP and microsatellite datasets, we removed individuals with more than a third of missing data, and kept only individuals for which we had both SNPs and microsatellites genotypes. We first compared genetic admixture between wild and captive-bred strains using STRUCTURE 2. 3. 1  [96]  with the admixture model and the correlated allele frequency model, without prior population information. Twenty runs assuming two clusters (K = 2, in order to discriminate between wild and captive-bred individuals, see  [94] ) were performed with a burn-in period of 200,000 and 200,000 subsequent MCMC repetitions. The ten best runs (highest LnP(D) values) were compiled using CLUMPP  [97]  to obtain final averaged individual Q-values. Individuals were assigned to one of the two clusters with the greatest Q-value, provided that value exceeded 0.