dataesr/scientific-paragraphs-categorization huggingface dataset:
The same number of markers (10,459,872 SNPs) were used in both analyses. The Jack-knife block approach was used to i) determine the standard deviation and variance of the D-statistic values and ii) rectify the correlations between loci caused by linkage disequilibrium. We applied Simon H. Martin script (https:// github. com/ simon hmart in/ genom ics_ gener al). We investigated where the introgression originated and where it was integrated. Using diversity trees created both inside the introgressions (in contrast to cA) and outside (in comparison with Japonica). To construct the trees, we used the NJ function of the APE package  (Paradis et al. 2004 ) which is based on neighbour-joining tree estimation of  Saitou and Nei (1987)   (Saitou and Nei 1987).
We focused only on chromosome 6. Polarization is necessary to define which of the two alleles is ancestral or derived. We chose not to polarise the data and instead considered the reference allele versus the alternative allele. Data were phased using the software BEAGLE V5. 2  (Browning and Browning 2007 ) using default settings. For a given reference allele, the integrated EHH (iHH) is defined as the area under the EHH curve, which in turn is defined by the EHH values and the chromosomal positions associated with each allele iHH_ref and iHH_alt  (Voight et al. 2006). iHS, defined as ln(iHH_ref/iHH_alt), is constructed to have an approximately standard Gaussian distribution, then the transformation of the iHS values into p-values is done according to the following equation: where Phi(x) is the distribution of the Gaussian cumulative distribution function.
Genetic Diversity Analysis In a pairwise comparison between subgroups, we computed the genome-wide fixation index (Fst) for each of the defined GJ-trp subgroups. We used the same set of markers as the Admixture analysis (74,373 SNPs). We used the compute pairwise. fst. dosage function from the hierfstat package  (Weir and Goudet 2017). We considered the same groups as in the TreeMix analysis, excluding the NERICA improved varieties, to calculate the diversity statistics along the chromosome. Because the Pi, D xy , and Tajima's D statistics are influenced by the number of samples, we have adjusted the group sizes as a precaution. Each group has roughly the same number of samples (15 or 16), with the exception of two groups, the accessions from Asia 2 (India and Sri p(iHS) = -log10[1 -2|Phi(iHS) -0.
Candidate Gene Identification and Characterization We applied a custom script to detect all underlying genes in the 30 kb of significant SNP markers in the iHS data with a p-value greater than 5. The gene list utilised was based on the 3 K-RG vcf annotation file, which can be found at https:// snp-seek. irri. org, and the funRiceGenes database, which contains over 4100 functionally defined rice genes and over 6100 gene family members  (Huang et al. 2022). We used Gene Ontology enrichment (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analysis to characterise the function of each candidate gene. The annotations of the list of identified genes were made on three different databases, namely:https:// shiny apps. south green. fr/ app_ direct/ goenr ich/, http:// bioin forma tics.
sdsta te. edu/ go/, and http:// geneo ntolo gy. org/. All annotations were based on genes from the O. sativa Japonica group. The gene ontology platform http:// geneo ntolo gy. org/ contains 35,775 annotated genes, http:// bioin forma tics. sdsta te. edu/ go/ 43,658 annotated genes. The SouthGreen database (https:// shiny apps. south green. fr/ app_ direct/ goenr ich/) is contains 23,733 and has roughly 910 K GO terms. All significant signalling pathways with an FDR less than 0. 05 were kept. The nominal P-value from the hypergeometric test is used to calculate FDR. Fold Enrichment is defined as the percentage of genes in our list that belong to a pathway divided by the corresponding percentage in the background. FDR reveals the probability of enrichment by chance. Fold Enrichment, as a measure of effect size, indicates how significantly genes from a specific pathway are overrepresented (for more details http:// bioin forma tics.
Acknowledgements This work has been realized with the support of  MESO@LR-Platform at the University of Montpellier  and  IFB Core cluster at IDRIS, CNRS, Orsay, France. We thank the  I-Site MUSE  for the financial support of this project. This work, part of the  MUSE AdaptGrass  project, was publicly funded through  ANR  (the  French National Research Agency)  under the  Investissements d' Avenir Programme  with the reference  ANR-16-IDEX-0006.
In the following relocations, we apply NLL-SSST with a smallest, Gaussian kernel smoothing length of 4 km or 2 km and apply NLL-coherence using waveforms up to 10 Hz or 20 Hz frequency (Supplementary File S1). We obtain formal NLL-SSST-coherence epicenter (errH) and depth (errZ) uncertainties as low as 100-200 m (Supplementary Table  S1  and Datasets S1-5 ). This uncertainty range represents the relative locations accuracy (precision) of the NLL-SSST-coherence relocations, but not the absolute location accuracy which may be much larger. As with other location procedures, NLL-SSSTcoherence does not directly improve absolute epicenter and depth accuracy on the largest scale (e. g. , the full study area), for which accurate velocity models and calibration with ground truth information is needed.
Acknowledgements We gratefully thank  Daniel Trugman , an anonymous reviewer and the editor  Stephen Hicks  for extensive comments that greatly improved this work. We thank  Alberto Michelini  and  Clément Perrin  for helpful comment and discussion, and  Egill Hauksson  for assistance with the high-precision, HYS catalog for the 2021 Calipatria sequence. Special thanks to the analysts, technicians and scientists who formed the highquality, NCEDC and SCEDC arrival-time and event catalogs which made the precision of this work possible. We use LibreOffice (https://www. libreoffice. org, last accessed April 2023) for word processing, spreadsheet calculations and drawings, and Zotero (https:// www. zotero. org, last accessed April 2023) for citation management. This work was performed outside any specific funding or grant.
Fake news, created with different intentions and disseminated through various social networks and messaging platforms, has become a significant means of disinformation in the current landscape. In the face of certain events or controversial situations, the number of fake new items increases, a situation that can become worrying. One such event was the arrival of the Covid-19 pandemic. To combat this phenomenon, various fact-checking bodies appeared with the objective of demonstrating and disseminating whether a certain piece of information was correct. At the international level, these organizations have joined together in the #CoronaVirusFacts/#DatosCoronaVirus Alliance. The organization Chequeado from Argentina has collected information coming from Latin American (Latam) countries plus Spain and Portugal in a specific database, which allows for their analysis on an independent basis.
The Poynter Institute's International Fact Checker Network (IFCN) launched the #CoronaVirusFacts Alliance in January 2020, at a time when the spread of the coronavirus was limited to China but already causing rampant misinformation around the world. This alliance united more than 100 fact-checkers from around the world to publish, share and translate facts related to the COVID-19 pandemic. The World Health Organization classifies this as an infodemic. The #CoronaVirusFacts database https://www. poynter. org/coronavirusfactsalliance collects all hoaxes discovered by the Alliance and will be updated daily with new publications until early 2023. It can be sorted, filtered and searched by content. The database brings together fact-checkers from more than 70 countries and includes articles published in at least 40 languages (Figure  2 ).
In April of that same year, also under the auspices of the #CoronaVirusFacts Alliance, a specific database began to be published with verified information on coronavirus from the different verifying agencies of Latin American (Latam) countries, plus those of Spain and Portugal, a subset of the previous one. Latam Chequea Coronavirus integrated 35 organizations from 15 countries 4 , to disseminate in Spanish and Portuguese the misinformation circulating about COVID-19, Figure  3. The project was coordinated by the organization Chequeado 5 (Argentina) and supported by Google News Initiative. Unlike the previous one, this database is free to download, available at: https://chequeado. com/latamcoronavirus The verification objectives of the organizations adhered to the Latam Check Coronavirus project can be summarized as follows: 1.
Visualization of fake news about COVID-19 Using visual elements such as graphs and maps, data visualisation provides an accessible way to identify and understand trends, outliers and patterns in data. It is used to provide simple summaries of large data sets. In visualisation, statistics is joined by other disciplines that help to highlight information to make it easier to understand. "The raw material to develop an effective visualization is data, figures and the search for information. And, thanks to information design, it is possible to take these variables and transform them into graphic pieces that will help the general public to perceive the relationships they have more easily" (Bayas-Ramírez, 2020). Thus, data visualisation on COVID-19 allows us to use another way of communicating information and methods that serve other disciplines (design, communication, data journalism, databases, etc.
) to analyse and make data accessible. Examples of visualisations of fake news data related to the SARS-CoV-2 pandemic include those using data collected by the #CoronaVirusFacts Alliance mentioned above. This alliance has created a database of more than 17,000 fake news verifications, which it presents in several types of interactive visualisations on its website. One of them (Figure  4 ) shows on a map the number of verifications per country since January 2020, a map updated at the end of each month. The distribution of the data logically reflects the contributors to the Alliance, as there is no direct data where there are no verifiers. As in the previous case, the different types stand out (in this case with an iconographic chart with the most common ones, Figure  6 ) and how they vary over time: They also use the geographical distribution of some hoax subtypes (Figure  7 ) and timelines to show how specific hoaxes spread across different countries (Figure  8 ).
Methodology and data source Social network analysis (SNA) (Avila-Toscano, 2018) has been used as a methodology for part of the data visualization. SNA uses networks and graph theory  (Andrienko et al. , 2020; Otte; Rousseau, 2002). The software used to create these visualizations has been Gephi (Bastian; Heymann; Jacomy, 2009): https://gephi. org Gephi is a program for visualizing, exploring, and understanding all types of graphs and networks  (Cherven, 2015). It is free, open source and has ARS as the basis of its operation. The spatialization algorithms used have been Atlas Force 2 and Atlas 2-3D. It has been combined with a viewer that allows exporting to the web the graphs made with Gephi, called gexf. js (Velt, 2011) and which is available on Github: https://github. com/raphv/gexf-js This viewer enables interactivity for multiple users and supports the inclusion of several plots.
SCImago Graphica (Hassan-Montero; De-Moya-Anegón; Guerrero-Bote, 2022) , and Excel (dynamic graphics) have also been used for other statistical analyses. https://www. graphica. app The data used comes from the collection conducted by the Spanish, Portuguese, and Latin American verification agencies (Latam Chequea Coronavirus network) belonging to the International #CoronaVirusFacts/ Network Alliance (IFCN) of the Poynter Institute mentioned above. Unlike the general data, this table can be downloaded in. csv format (comma separated values) for free use, with acknowledgement of authorship. It is the one that has been used in this work as the basis for data analysis, graphs, and visualizations (Figure  3 ). Although it is called a database, it is a single table with structured data: the rows contain the different records, and the columns contain the different data (a database interrelates two or more tables).
Geometry of the Ecological Niche We developed a geometric approach to the concept of genomic offset (GO) by defining a dot product of ecological predictors built on effect sizes of those predictors on allelic frequencies. Effect sizes, (b ℓ ) = (b ℓj ), were obtained from a GEA model of centered allelic frequencies on scaled predictors observed at a set of sampling locations. In that notation, ℓ stands for a locus, and j stands for a predictor. Effect sizes were corrected for the confounding effects of population structure and missing predictors (Methods: "GEA studies"). Given d ecological predictors, recorded in vector x, and their altered versions based on some change in time or space, recorded in x ⋆ , we defined a geometric GO-implemented as genetic gap in the computer package LEA-as a quadratic distance between the two vectors x and x ⋆ G 2 (x, x ⋆ ) = (x -x ⋆ )C b (x -x ⋆ ) T , (1) where C b = E[b T b] is the empirical covariance matrix of environmental effect sizes.
Improving GO Statistics According to  Rellstab et al. (2021) , current GO statistics may provide wrong predictions due to the correlation between population structure at selectively neutral loci and environmental predictors. Built on unbiased effect sizes, the geometric GO, which is based on a unique model for GEA estimation and for GO prediction, addressed this problem by including latent factors as covariates in the prediction model. Latent factor corrections were then incorporated into all considered GO statistics, which increased their predictive performance compared with their traditional usage. Our versions of RDA GO and Rona-that slightly differ from original proposals-were implemented in the R package LEA. Although those changes led to improved statistics, the geometric GO reached higher predictive performance than the other GO approaches.
Conclusions Considering a duality between genetic space and environmental space, we developed a theoretical framework that linked GO statistics to a non-Euclidean geometry of the ecological niche. The geometric GO, as well as the modified Rona statistic, were implemented in the genetic gap function of the R package LEA  (Gain and François 2021). As a result of the quantitative theory, interpretations in terms of fitness in the altered environment were proposed, unifying several existing approaches, and addressing some of their limitations. Based on extensive numerical simulations and on data collected in a common garden experiment, our study indicated that GO statistics are important tools for conservation management in the face of climate change.
GEA Studies GEA studies and estimates of environmental effect sizes were performed based on LFMMs in the computer package LEA v3. 9  (Caye et al. 2019; Gain and François 2021). In LFMMs, allelic frequencies are modeled at each genomic locus of a genotype matrix as a mixed response of observed environmental variables with fixed effects and K unobserved latent factors. The number of latent factors was estimated from the screeplot of a principal component analysis of the genotype matrix. Loci with minor allele frequency less than 10% were filtered out the analysis. Statistical significance was determined by using the R package qvalue at a level of false discovery rate equal to 10%.
GO Computations RDA was performed by using principal components of fitted values of the GEA regression model. Rona was computed as the average value of the absolute distance between predicted allelic frequencies across genomic loci  (Rellstab et al. 2016; de Aquino et al. 2022). GF computations were performed using the R package gradientForest version 0. 1. For consistency, we reported squared values of GO statistics in RDA and GF. Unless specified, GO statistics were computed on the loci detected in the GEA study, that is, a same set of loci for all methods. To correct statistics for the confounding effect of population structure, all analyzes were performed conditional on the factors estimated in the LFMM analysis (Supplementary Material: "GO computations").
Climate data were used to compute 157 metrics in three categories, precipitation, temperature (mean, maximum, and minimum near surface air temperature), and surface downwelling shortwave radiation, that were reduced by principal component analysis (27 axes). GO statistics were computed using the climate condition (x) at the location of origin of the landrace and the climate conditions (x ⋆ ) at the experimental site. For each GO statistic, we estimated a linear relationship with the logarithm of the mean total weight of seeds and used Pearson's squared correlation to evaluate the goodness of fit. The J-test was used to test differences between predictive performances, corresponding to R-squared for distinct regression models, of the geometric GO and other GO statistics  (Davidson and MacKinnon 1981).
Acknowledgments This work received support from the  French National Research Agency , projects  PEG  (grant number  ANR-22 -CE45-0033 ),  Afradapt  (grant number  ANR-22-CE32 -0008 ), and  ETAPE  (grant number  ANR-18-CE36-0005 ). The authors are grateful to  Thibaut Capblancq  for many interactions and fruitful discussions.
Data Availability The pearl millet data have already been published and have permissions appropriate for fully public release. The codes necessary to reproduce the simulations and data analyses of this study are available at https://github. com/ bcm-uga/geneticgap under GNU General Public License v3. 0 The geometric GO is implemented in the genetic gap function of the R package LEA (version number >3. 9. 5) available from the public repository bioconductor and https://github. com/bcm-uga/LEA (latest version).
Scenarios of climate change ScenarioMIP  (O'Neill et al. , 2016)  chose eight particular Shared Socioeconomic Pathways (SSPs) taken from the SSP scenario database  (Riahi et al. , 2017)  to cover a range of socio-economic assumptions and climate targets. After harmonization, these SSPs became the default CMIP6 scenarios to be run by ESMs  (Gidden et al. , 2019). ScenarioMIP mostly required concentration-driven simulations up to the year 2100 or 2300. In RCMIP, this was complemented by extending all scenarios up to 2500 and systematically running emission-driven simulations in addition  (Nicholls et al. , 2020). Figure  6  displays projections of key global variables of the Earth system following these scenarios, and Table  5  focuses on projected GSAT changes. The climate target dimension of the SSP scenarios is defined similarly to the RCPs' as the total RF targeted in 2100  (van Vuuren et al.
Acknowledgements. The simulations were performed on the ebro cluster at IIASA. We also thank  Zebedee Nicholls  for providing us with the time series of global surface air temperature of  CMIP6. Financial support. This research has been supported by the  FP7 Ideas: European Research Council  (grant no. ERC-2013-SyG-610028 ), the  H2020 European Research Council  (grant no. 820829 ) and the  Austrian Science Fund  (grant no. P31796-N29 ). Review statement. This paper was edited by  Gunnar Luderer  and reviewed by two anonymous referees.
Completeness of the transcriptome was verified using BUSCO v5. 2. 2 running in transcriptome mode with the eukaryote_odb10. 2019-11-20 reference gene set  (Simão et al. , 2015). Maullinia proteome was inferred using the longest open reading frames and the protein coding genes predicted by Transdecoder v5. 0. 2 (https://github. com/TransDecoder) with default settings and used in downstream analyses. Functional annotation of the predicted genes was achieved using InterProscan v5  (Jones et al. , 2014).
Additional molecular data Transcriptome data from an Austrian population of Plasmodiophora brassicae were taken from  Ciaghi et al. , 2018b. Publicly available genomic data were taken from P. brassicae strains e3  (Schwelm et al. , 2015)  and PT3  (Rolfe et al. , 2016) ; and Spongospora subterranea strain K13  (Ciaghi et al. , 2018a). In silico predictions of trophic mode BUSCO v5. 2. 2 was run in proteome mode against the eukaryote_odb10. 2019-11-20 reference gene set  (Simão et al. , 2015)  to assess completeness of all inferred proteomes, allowing for accurate predictions of trophic modes  (Liu et al. , 2021). Genomic and transcriptomic data from all three species of phytomyxean parasites in this study show a high degree of BUSCO completeness (< 105 missing BUSCOs over the total 255 BUSCOs in the eukaryota_odb10 database), indicating that their trophic mode can be accurately assigned by TrophicModePredictionTool (Supplementary Figure  1 ).
The TrophicModePredictionTool tool  (Burns et al. , 2018)  was used to predict the trophic mode of the investigated organisms in silico, based on the molecular signatures for phagocytic, photosynthetic, and prototrophic capabilities (i. e. organisms capable of synthetising arginine, lysine, threonine, biotin, vitamin B1, B2 and B6). The code (available at https://github. com/burnsajohn/predictTrophicMode) was run in the default mode. Prediction scores enumerate the probability that an organism has the genetic toolbox to carry out the indicated function on a scale of 0 to 1. A probability above 0. 5 suggests that an organism has the capacity to utilise the indicated function, higher scores indicate a higher degree of confidence. Besides the three main trophic modes listed above, special form of phagocytosis such as that of the extracellular parasite Entamoeba histolytica and of the intracellular parasite Rozella allomycis are predicted via an emended subset of molecular signatures of phagocytosis.
Results In silico prediction of trophic modes of Phytomyxea using genomic and transcriptomic signatures All analysed phytomyxids datasets bear molecular signatures of phagotrophy (Fig. 1 , Supplementary Table  1 ). Plasmodiophora brassicae (e3 and PT3) and S. subterranea (SSUBK13) genomes score high for phago-prototrophy (red numbers 11, 12 and 18 on the Mollweide projection in Fig. 1 ). The prediction scores from the genome data in P. brassicae are approximatively 60% for prototrophy (e3 = 0. 615 and PT3 = 0. 612) and are similar for general phagotrophy (e3 = 0. 700; PT3 = 0. 600). The prototrophy score for Spongospora subterranea is lower (SSUBK13 = 0. 500, bar chart in Fig. 1 ), as is the score for general phagotrophy (SSUBK13 = 0. 552). When the subset of signatures predicting Rozella-like intracellular phagotrophy is considered the probability scores increase to nearly 100% for the genome datasets (e3 = 0.
978; PT3 = 0. 983 and SSUBK13 = 0. 967; bar chart in Fig. 1 ). The probabilities for photosynthesis and entamoebid-like phagotrophy (a second peculiar mode of phagotrophy mostly observed in extracellular endoparasites such as Entamoeba) remain below the threshold of 50% in the genomic data (Supplementary Table  1 ). When the proteomes inferred from the transcriptomes of P. brassicae and M. ectocarpii are tested the score for general phagotrophy and prototrophic predictions are very low: in P. brassicae the prediction score for general phagotrophy decreases to 0. 018 and prototrophy to 0. 087, in M. ectocarpii phagotrophy scores 0. 209 and prototrophy 0. 179. The Rozella-like phagotrophy remains high with a score of 0. 838 in P. brassicae and 0. 894 in M. ectocarpii (bar chart in Fig. 1 ). Presenting these data as a Mollweide projection, the transcriptome datasets are placed with other "Parasite" mapping close to the intracellular fungal parasite Rozella allomycis (red numbers 9 and 13 and black number 16 in the Mollweide projection in Fig.