dataesr/scientific-paragraphs-categorization huggingface dataset:
domesticus italiae). However, under Clements taxonomy, P. italiae is considered a separate species. In this instance, we directly transferred sexual selection and data certainty scores from the BirdTree "P. domesticus" values to the Clements "P. domesticus" values. For P. italiae, we used the same sexual selection score as the parent by inference, because breeding behaviour of this geographically restricted form appears similar to the more widespread P. domesticus. Nonetheless, we dropped the data certainty score from 4 to 3 reflecting the reduction in certainty that arises because P. italiae occupies a small geographical range and the sexual selection score is no longer based on direct species-specific evidence. The reverse procedure was required in cases where multiple BirdTree species have been merged into a single Clements species.
When transferring data from BirdTree to Clements, 31 species presented challenges because data certainty scores differed between daughter taxa being merged together (lumped) into a single parent taxon. In most of these problematic cases, the sexual selection score of widespread parent taxa was based on direct data from primary sources, whereas this direct data often only applied to one of the BirdTree daughter species (splits). In these instances, we transferred the certainty data from the nominate BirdTree species to the lumped (parent) eBird species. The only exception to this rule was Stercorarius antarcticus which is considered two separate species (Catharacta lonnbergi and Catharacta antarctica) by BirdTree and a single species by Clements. Under BirdTree, the daughter species or split, C.
Stercorarius). In five cases, BirdTree species were initially lumped and then subsequently resplit into multiple daughter species. However, sexual selection happened to be scored as strict monogamy (0) in all these cases, simplifying the direct transfer of scores from BirdTree to Clements datasets. Finally, 176 Clements species had no corresponding BirdTree species. The majority of cases (n = 144 species) were extinct species that were omitted from BirdTree, which we did not include in our database. The remaining 32 species were described in the last decade, representing new additions to global species lists. In these cases, we scored sexual selection from literature following the same protocol used for BirdTree species (see Methods). We include a taxonomic crosswalk (sheet 4) in Supplementary dataset 1.
This resource lists each BirdTree and Clements species aligned in neighbouring columns, showing how they are matched in our data transfer process. Specifically, we include the type of link between the two taxonomies (one-to-one, many-to-one, one-to-many, manyto-many) and highlight the parent taxon in cases where data are duplicated across multiple taxa in the case of splits. The crosswalk is designed to be used alongside the R script (clements_conversion. R) included with this study. As a disclaimer, our crosswalk may contain small errors as it was designed to transfer traits between thousands of species. We recommend that users carefully check the conversion of traits, paying particular attention to species which have transitioned between families or orders. We hope that the Clements dataset and associated taxonomic crosswalk can support future researchers by facilitating the transfer of traits to and from Clements taxonomy, including cases where lumped BirdTree species have conflicting data.
Sexual selection metrics To assess the reliability of our scoring system, we collected data on three complementary and widely used measures of sexual selection from existing published datasets: residual testes mass  (Pitcher et al. 2005; Baker et al. 2020) , Bateman gradients (βSS)  (Janicke et al. 2016; Fromonteil et al. 2023) , and the opportunity for sexual selection (IS)  (Janicke & Morrow 2018). Residual testes size constitutes a commonly used proxy for the strength of post-copulatory sexual selection on males whereas βSS and IS are standardized metrics for the strength of pre-copulatory sexual selection. To increase our sample size, we also calculated metrics for additional species where possible, based on published data. Residual testes mass refers to the mass of testes after accounting for total body mass.
We calculated IS for 28 unique species, either directly from information presented in papers, or from data subsequently provided by authors. Given the scarcity of studies that calculate quantitative measures of sexual selection, we also estimated IS for 51 monogamous species that recorded 0% extrapair paternity (EPP), indicating true genetic monogamy. Assuming that each individual was paired, and that operational sexratios are approximately even (as tends to be true for genetically monogamous species), this meant we could assign an IS of zero for these 51 additional species. As a sensitivity analysis, we removed these species from our reliability assessment (see main Methods), which produced near-identical results (Table  S1 ). Bateman gradients (also termed sexual selection gradients) represent the slope of an ordinary least squares regression of reproductive success (number of offspring) on mating success (typically quantified as the number of mating partners) and therefore estimate the fitness benefit that can be obtained from additional matings.
Spatial methods To understand how sexual selection varies globally, we incorporated spatial information using expert-drawn geographical range maps provided by Birdlife International (2021). To align species with BirdTree  (Jetz et al. 2012) , we used the BirdLife-to-BirdTree taxonomic crosswalk provided in AVONET  (Tobias et al. 2022). In most cases, BirdTree species had a single equivalent BirdLife species name (one-to-one matches; n = 8949), which allowed us to use the corresponding geographical range polygon for the same species. The majority of taxonomic differences between BirdTree and BirdLife were splits, which resulted in a single BirdTree species corresponding with multiple BirdLife species (BirdLife splits; n = 1929). In these cases, we combined multiple BirdLife daughter species into a single range polygon for their corresponding parent BirdTree species using the sf package  (Pebesma 2018 ).
In the small number of cases where multiple BirdTree species correspond with one BirdLife species (BirdLife lumps; n = 198), we used BirdLife version 2. 0 maps (BirdLife International 2012), created prior to any taxonomic changes. In this resource, we used maps for the corresponding BirdLife synonyms for each BirdTree species. For a small number of BirdLife lumps (n = 64), no previous geographical range polygon existed as a version 2. 0 map. In addition, some taxonomically stable species lacked geographical data (n = 57), either because no part of their known range is coded as resident or breeding range, or because the map is not openly released by BirdLife International (typically because the species is sensitive to hunting or trapping for the cage-bird trade). For mapping purposes, we restricted range polygons to areas where the species is coded by BirdLife International as extant and either native or reintroduced.
Finally, because latitude and environmental conditions in the nonbreeding range are largely irrelevant to levels of sexual selection, we limited our selection to the breeding and resident ranges of each study species. To extract species-specific data, we used a Behrmann equal-area projection for geographical range polygons. To calculate a single value of latitude for each species, we determined the geometric centroid of each range polygon using the PBSmapping package  (Schnute et al. 2017). This approach was preferred over midpoint latitude metrics because the centroid considers the overall shape of a species' distribution, particularly important for wide-ranging species. Assigning a single value of latitude to each species greatly over-simplifies their distribution, and can be misleading, particularly in species with wide ranges spanning the equator.
Bayesian ordinal models Bayesian mixed-effect models were constructed using the brms package in R  (Bürkner 2017). Because our sexual selection scores are rankings, we used ordinal regression models with a cumulative family distribution. This model estimates the cumulative odds of being at or below each category level, providing insights into how the independent variables influence the likelihood of moving up or down the ordinal scale. Coefficient estimates and confidence intervals can be interpreted similarly to other standard generalised-linear models. Posterior predictive checks (Fig. S2 ) confirmed that a cumulative family distribution fits the data extremely well. To first establish if there were latitudinal gradients in sexual selection, we used univariate models with sexual selection score as the response and absolute latitude as the predictor.
We chose not to apply any additional transformations to latitude because models fit generally well, and this allowed for the best alignment with maps of sexual selection. We first ran a single model predicting sexual selection strength for all species (n = 9836), which showed a strong positive gradient in sexual selection (Table  S2 ). Given the strong positive effect of latitude on sexual selection across species, we ran subsequent univariate models to determine if the overall pattern was true for different ecological groups, using the same response and predictor for different data partitions. We then repeated each model using a high certainty dataset (certainty score 3 -4), which produced similar results. To reduce spatial autocorrelation, we performed a sensitivity analysis by dichotomizing latitude as either tropical (centroid latitude within the Tropics of Capricorn and Cancer) or non-tropical.
Acknowledgements We thank  Jane Amirthanayagam ,  Jessamine Badcock-Scruton ,  Celina Chien ,  Anita Kristiansen ,  Xiaoya Lian  and  Thomas Munro  for their help in data collection and management. Emily DuVal  contributed valuable unpublished data. Illustrations from Birds of the World are reproduced with permission of  Cornell Lab of Ornithology. This study was funded by a  Natural Environment Research Council   PhD scholarship  to RAB ( Doctoral Training Centre :  NE/P012345/1 ).
Data availability Data for all analyses are available in Supplementary Data 1. In addition, phylogenetic trees were downloaded from http://www. birdtree. org, with the specific subset of trees used in the analyses presented in Supplementary Data 2. Climate data was extracted from the CHELSA bioclim dataset v2. 1 (https://chelsa-climate. org/bioclim/) and species range maps were provided by BirdLife International (http://www. datazone. birdlife. org). Silhouettes used in figures are public domain (sources given in Supplementary Data 2).
Extended Data Fig. 6. Variation in sexual selection for tropical and non-tropical species. Models predicting the difference in sexual selection strength between tropical and non-tropical species, partitioned by ecological trait. Black points show estimated sexual selection for each group, extracted from species-level bayesian regression models; whiskers show 95% confidence intervals from model predictions. Additional species-level models on a high certainty subset (scored 3-4) showed similar patterns (Table  S3 ; Supplementary Information). Numbers above whiskers are sample sizes (number of species).
When we ran a sensitivity analysis (IS2) with these arbitrary zeroes excluded, results remained similar. Models were run on a sample of 50 phylogenetic tree topologies extracted from www. birdtree. org  (Jetz et al. 2012)   (strict monogamy) to 4 (extreme polygamy; see Extended Data Table  1 ) and data certainty is scored from 1 (no direct or indirect evidence) to 4 (direct evidence published in primary and secondary literature; see Extended Data Table  2 ). In    2 ). Cell values represent the total number of high-certainty species, calculated by counting the number of geographical range maps overlapping more than 50% of each 5-km grid cell. Cell values were grouped into equal-sized bins to reduce the skew in the colour scale caused by outlier cells with high species richness. Cells with <10 species were excluded.
Contact. Strong lenses are a biased subset of the general population of galaxies. Aims. The goal of this work is to quantify how lens galaxies and lensed sources differ from their parent distribution, namely the strong lensing bias. Methods. We first studied how the strong lensing cross-section varies as a function of lens and source properties. Then, we simulated strong lensing surveys with data similar to that expected for Euclid and measured the strong lensing bias in different scenarios. We focused particularly on two quantities: the stellar population synthesis mismatch parameter, α sps , defined as the ratio between the true stellar mass of a galaxy and the stellar mass obtained from photometry, and the central dark matter mass at fixed stellar mass and size. Results. Strong lens galaxies are biased towards higher stellar masses, smaller half-mass radii, and higher dark matter masses.
2020; Birrer & Treu 2021) , but any difference between the two lens classes can introduce biases if not corrected for. With this study we aim to quantify this bias. The structure of this paper is as follows. In Sect. 2 we introduce the basics of gravitational lensing. In Sect. 3 we study individual lens systems. In Sect. 4 we describe our simulations of lens surveys. In Sect. 5 we show the results of our analysis of the simulated lens survey data. We discuss the results in Sect. 6 and draw conclusions in Sect. 7. The Python code used for the simulation and analysis of the lens sample can be found in a dedicated section of a GitHub repository 2.
Quasars We described the population of background quasars with the following double power-law luminosity function in the rest-frame UV absolute magnitude, M: Φ(M, z qso ) = Φ(M * ) 10 0. 4(α Q +1)(M-M * ) + 10 0. 4(β Q +1)(M-M * ) • (28) Following  Manti et al. (2017) , we set α Q = -1. 35 (faint-end slope), β Q = -3. 23 (bright-end slope), and adopted a redshift-5 https://github. com/ICRAR/shark evolving normalisation log Φ * = -6. 0991 + 0. 0209z qso + 0. 0171z 2 qso (29) and characteristic magnitude M * = -22. 5216 -1. 6510z qso + 0. 2869z 2 qso. (30) Given the redshift and rest-frame UV luminosity of a quasar, we then computed the apparent magnitude in the observed iband, m qso , using a quasar spectral template 6  built from optical and near-infrared spectra obtained by Vanden Berk et al.
Acknowledgements. The collaboration leading to this work was initiated at the  2022 Lorentz Center  workshop " Bridging gaps between dynamical probes of galaxies ". A. S. and S. S. L. are supported by  NOVA , the  Netherlands Research School for Astronomy. Support for this work was provided by  NASA  through the  NASA   Hubble Fellowship  grant  HST-HF2-51492  awarded to AJS by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555.
Results Identification of a ThyX orthologue in Candidatus Prometheoarchaeum syntrophicum strain MK-D1 (Psyn) As an initial approach to investigate the Asgard archaeal thymidylate and C 1 metabolism, we searched for thyX and thyA homologs in sequence databases for archaeal genomes or metagenome-assembled genomes (MAGs). These studies led to the identification of both thyX and thyA sequences in the genomes of the understudied and diverse group of Asgard archaea. Interestingly, our similarity searches identified a ThyX candidate in Psyn (Supplementary Fig. 1 ), which is up to 51. 44% identical to bacterial ThyX sequences at the protein sequence level (Zixibacteria, e-value ≈10 -78 or Calditrichaeota bacterium, e-value ≈10 -76 ). The observed e-values are very low and are adjusted to the large sequence database size.
1c , left panel). Structural superposition of the model with PDB structure 4GT9 using the Chimera software 27 allowed the addition of FAD, dUMP and folate molecules into the active site of the model (Fig. 1c , middle panel). Importantly, the model suggests the highly plausible transfer of 1C units via the N5 atom of the FAD cofactor to the accepting carbon of dUMP (Fig. 1c , right panel).
ThyA and ThyX genes are inserted in archaeal-like genomic contexts To exclude the possibility that Asgard metagenomes and genome assemblies are contaminated with foreign DNA sequences, we analysed the taxonomic origins of the genomic environments of the Asgard thyX and thyA genes against the NCBI reference sequence database (Fig. 5a ). An important fraction of the genes surrounding Asgard thyX are unambiguously of archaeal origin (Fig. 5a ): for one third of Asgard contigs, the majority of the best hits corresponds to archaeal sequences. A very similar pattern is observed for the Asgard contigs carrying thyA (Fig. 5a ). By focusing on the genomic context of the Psyn thyx gene (Supplementary Fig. 1 ), we show that almost all of the 18 genes upstream or downstream have their first BLAST hit matching with an archaeon.
Bioinformatics methods and genome-wide analyses The complete genome sequence available for the Candidatus Prometheoarchaeum syntrophicum strain MK-D1 (Psyn) chromosome (NZ_CP042905. 1) was used for bioinformatics analyses. This genome is 4278 Mb with a GC content of 31. 2%. RPKM values for gene expression were determined using the Geneious Prime® 2021. 1. 1 (Build 2021-03-12) and agreed well with the previously published values. Metabolic reconstructions were performed using the RAST server and KEGG databases. These analyses were complemented with highly sensitive manual similarity searches using HHpred  50.
Structural alignments and structure modelling PROfile Multiple Alignment with predicted Local Structures and 3D constraints (PROMALS3D) was used to align the Psyn ThyX sequence with the Thermotoga (T. ) maritima ThyX structure (PDB 5CHP). PROMALS3D (http://prodata. swmed. edu/promals3d/promals3d. php) aligns multiple protein sequences and/or structures, with enhanced information from database searches, secondary structure prediction, and 3D structures. Input sequences and structures used FASTA and PDB formats, respectively. (red: alpha-helix, blue: beta-strand). A model of the Psyn ThyX homotetramer was constructed with the protein structure modeling program Modeller using PDB structures 1O26, 3N0B, and 6J61 as templates  26. Ten initial models were constructed and the best one was chosen using molpdf and DOPE score, and by evaluating the stereochemical quality with PROCHECK.
Automated prediction of HGT Automated analyses of potential HGT events were performed using a HGTector  28. This method first performs Diamond all-against-all similarity searches of each protein-coding sequence of Psyn. The NCBI genome database was downloaded and compiled (on August 2021) on a local machine. The output of the similarity searches was passed to the analysis program HGTector where the taxonomy of the self-group was defined as Candidatus Prometheoarchaeum syntrophicum (NCBI TaxID: 2594042). The close-group was automatically inferred by HGTector as the "superkingdom Archaea" (TaxID: 2157) that contains 1062 taxa in the reference database. From the statistical distribution of close and distal hits, HGTector predicted a list of potentially transferred genes. The output of the program is a scatter plot, where the horizontal axis represents the close score, the vertical axis represents the distal score, and potential HGT genes are colored in yellow.
Phylogenetic analyses For phylogenetic analyses, we constructed a database with a backbone of 183 thyX sequences that cover all the major bacterial and archaeal phyla derived from a previous study  13. Homologs found in Asgard genomes were then used as seeds for reciprocal BLAST searches against an NR database (15/01/2021 version) to find the best homolog outside Asgards. Finally, identical sequences (same identifying code or 100% sequence similarity) were removed to generate the final data set. We used similar approaches with the other folate enzymes to find homologous sequences in Asgard genomes. Phylogenetic analyses were performed using the obtained protein sequence data sets (see above) that were aligned using MAFFT v7. 388 with default settings  53. Ambiguously aligned sites were removed using trimAl v1.
4. rev15 with the "-automated1" and "-phylip" options. The phylogenetic position of the Asgard sequences was then inferred with two different Maximum likelihood softwares: PhyML v. 1. 8. 1 (Supplementary Fig. 13  for ThyA and ThyX) and IQTREE  54  using the best substitution models as determined by Smart Model Selection  48  and ModelFinder  55  , respectively. 1000 bootstrapped data sets were used to estimate the statistical confidences of the nodes using the Ultrafast option as implemented in IQTREE and the non-parametric bootstrap option in PhyML. Trees were then visualized using the iTol software  56. To investigate which branches the transfer events most likely involved, reconciliation analyses were computed with ecceTERA  57. SylvX  58  was used to visualize the reconciliation trees computed by ecceTERA.
Analysis of the genomic environments of the ThyA and ThyX genes Metagenomic Asgard contigs encoding thyA and thyX genes larger than 10 kb were retrieved and gene annotation were carried out using RAST  59. The taxonomic group of each protein-encoding genes was determined as follows : similarity searches against the NCBI RefSeq database (02/09/2021 version) were computed using Diamond  60  with blastp option and e-value cuttoff of 1e-10. The ten best hits of each gene were retrieved and the taxonomic profiling of these hits (Bacteria/Archaea/Eukarya) was collected using the PYlogeny software (https://github. com/jrjhealey/PYlogeny). For analysis of the genomic environment of the Psyn thyX gene, a more in-depth analysis was carried out by collecting all the genes located 15 kb downstream and 15 kb upstream of the Psyn thyX and searched for homologs with BLASTP against an NR database (15/01/ 2021 version).
The first BLASTP hit was retrieved, as well as the percentages of the archaeal sequences in the taxonomy profile of the first top 100 hits. Analysis of the synteny of the Asgard contigs encoding thyA and thyX genes were conducted using the Clinker package  61  with default parameters except the minimal identity thresholds that have been reduced to 25% (« -i 0. 25 » option). Design and synthesis of Psyn ThyX expression plasmid pTwist-Psyn-ThyX plasmid (3370 bp), carrying the synthetic Psyn thyX gene of 897 bp was synthesized by Twist Biosciences (https://www. twistbioscience. com) and confirmed by sequencing. In the final design, the target gene Psyn thyX was placed under the control of a strong bacteriophage T5 promotor carrying lacO sites. This construct also carried an appropriate ribosome binding site, a lambda t0 transcriptional terminator, and the Amp R gene.
Acknowledgements We thank  P. Forterre  for discussions on the evolution of DNA as a genetic material and  Y. I. Wolf  ( National Center for Biotechnology Information, U. S. National Library of Medicine, National Institutes of Health ) for advice and guidance in analyzing Asgard (meta)genomes. Z. L. is a student of the  Bachelor of Science program  of E. Polytechnique. H. M. , H. B. and U. L. thank  CNRS, INSERM , and  E. Polytechnique  for their financial support. RZ has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  899987. W. Y. is a recipient of a doctoral grant from  China Scholarship Council (CSC).
Data availability The bioinformatic data generated in this study have been deposited in Figshare https://doi. org/10. 6084/m9. figshare. 20067425. v2). These include results from sequence alignments, Blast searches and reconciliation analyses, IQTree and phyML trees as well as contig annotations, taxonomy data and protein accession numbers. The biochemical data for Fig. 7  are provided in the Source Data file. Source data are provided with this paper.