dataesr/scientific-paragraphs-categorization huggingface dataset:
Convolution to Limiting Resolution For all analyses, the Herschel and Σ H maps were convolved to a common resolution, to match whatever the worst resolution was among all of the data for each galaxy. We created conversion kernels for this with the Python package photutils. For the input Herschel PSFs, we used the azimuthally averaged beams from  Aniano et al. (2011). 7  For the input Σ H PSFs, we assumed Gaussian beams, as per Section 2. 3. For M31 and M33, the limiting resolution was the 36″ of the 500 μm maps; for the LMC and SMC, the limiting resolutions were dictated by the gas observations (see Section 2. 3 above), being 1′ for the LMC, and 2 6 for the SMC. We also rebin all maps to use a pixel width equal to that galaxy's limiting resolution, so that every pixel is statistically independent.
Decleir et al. (2023, in preparation) , but are provided here for the purposes of reproducibility. This full data set is available on Zenodo at doi:10. 5281/ zenodo. 7392275. The feathered Herschel maps, as presented in Paper I, can also be accessed at the NASA/IPAC Infrared Science Archive at doi:10. 26131/IRSA545 (Clark 2021).
This research benefited throughout from the constructive, thoughtful, and friendly input (and research environment), provided by the ISM * @ST group, 21  whose help made this a better paper; with particular thanks to C. Murray, J. Wu, and C. Zucker. This research made use of Astropy, 22  a communitydeveloped core Python package for Astronomy  (Astropy Collaboration et al. 2013 , 2018). This research made use of reproject, 23  an Astropy-affiliated Python package for image reprojection. This research made use of Photutils, 24  an Astropy-affiliated package for detection and photometry of astronomical sources  (Bradley et al. 2020 ). This research made use of NumPy 25  (van der  Walt et al. 2011; Harris et al. 2020) , SciPy 26    (Jones et al. 2001; Virtanen et al. 2020) , and Matplotlib 27    (Hunter 2007).
This research made use of the pandas 28  data structures package for Python  (McKinney 2010). This research made use of corner, 29  a python package for the display of multidimensional samples (Foreman-Mackey 2016). This research made use of iPython, an enhanced interactive Python  (Pérez & Granger 2007). This research made use of sequential color-vision-deficiency-friendly color maps from cmocean 30    (Thryng et al. 2016)  and CMasher 31  (van der Velden 2020) This research made use of TOPCAT 32  (Taylor 2005), an interactive graphical viewer and editor for tabular data. This research made use of the SIMBAD database 33   (Wenger et al. 2000)  and the VizieR catalog access tool 34   (Ochsenbein et al. 2000) , both operated at CDS, Strasbourg, France. This research has made use of the NASA/IPAC Extragalactic Database 35 (NED), operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA.
This research has made use of the NASA/IPAC InfraRed Science Archive 36 (IRSA), which is funded by NASA and operated by the California Institute of Technology. This research made use of the HyperLEDA database 37   (Makarov et al. 2014). Facilities: Herschel, Planck, IRAS, COBE, Swift, Parkes, Arecibo, VLA, ATCA, Effelsberg.
Artificial intelligence (AI) technologies have advanced to the point where they can automate large parts of the hiring process and in consequence transform the role of recruiters or Human Resource professionals. However, there is little research on how recruiters perceive AI, and little is known about the motivations or factors that influence the use of AI in their workplaces. This study examined recruiters' intentions to use AI by extending the unified theory of acceptance and use of technology (UTAUT) to include the frequency of AI use and education. Data were obtained from a web-based survey with 238 demographically balanced participants. Hierarchical regression analysis was applied for data analysis and hypothesis testing. The results showed that behavioral intention was significantly and positively influenced by performance expectancy, and the moderating effect of frequency of AI use, while gender, age, experience, and education had no significant impact.
These eight models were empirically compared using within-subjects, longitudinal data from four companies to formulate UTAUT based on conceptual and empirical similarities  (Venkatesh et al. , 2003). UTAUT distinguishes between intention to use (behavioral intention) and actual use (use behavioral), with behavioral intention directly influencing actual use. It is argued that there are four main determinants (or predictors) of technology use: effort expectancy (EE), performance expectancy (PE), and social influence (SI). These are direct determinants of behavioral intention and additionally facilitating conditions as direct determinants of usage behavior  (Heikkilä & Smale, 2011). In addition to the determinants, there are four moderators, namely gender, age, experience, and voluntariness of use, which indirectly affect the behavioral intention to use and ultimately the use of the new technology  (Bano et al.
Research method In this study, a mixed methodology was used, as both quantitative and qualitative data were collected. The quantitative approach was chosen to confirm or reject the proposed hypotheses H1-H5. The qualitative method provides a more complete picture and better insights into AI recruitment from the recruiter's perspective than a purely quantitative approach. The main UTAUT predictors (performance expectancy (PE), effort expectancy (EE), and social influence (SI)) that have a direct impact on behavioral intention (BI) were measured using 5-point Likert scale questions in a web-based survey. Moderating variables included age, gender, education, length of experience with AI tools, and additionally, frequency of using AI tools.
Data sample Primary data collection was conducted in October 2022 using a selfmanagement questionnaire designed for an online survey and distributed to randomly selected recruiters and HR professionals via the Prolific Academic (www. prolifc. co). Prolific Academic is explicitly designed for scientific use and has already been used successfully in various fields, such as economics, psychology or even food science. It combines transparency with high internal reliability of the psychometric scales and explicitly informs participants that they are being recruited for scientific research  (Palan & Schitter, 2018a , 2018b).
This scale is considered a reliable tool for behavioral research, and according to a European study, a 5-point scale is highly recommended as it provides better data quality compared to a 7-or 11-point scale  (Revilla et al. , 2014). In addition to the "agree" or "disagree" questions (AD) or the 5-point scale, respondents were asked to comment on their perceptions of AI tools and the advantages and disadvantages of AI technology in recruitment. Responses to these questions were coded, categorized, and interpreted to provide sufficient explanation for the results of the questions from AD. Stata/SE software (17. 0) was used to perform all statistically relevant calculations. Gender is coded as 0 for male, 1 for female, and 2 for other. Age is coded as 1 for 18-24 years, 2 for 25-34 years, 3 for 35-44 years, 4 for 45-54 years, and 5 for over 55 years.
The results of  Laurim et al. 's (2021)  study suggest that recruiters' motivation to use AI in their daily work is strongly influenced by a system's ability to improve individual job performance. This relates to the automation of operational tasks such as scheduling interviews or searching for candidates in databases. These results are consistent with the findings of this research, which highlighted the influence of performance expectancy on the intention to use AI in recruiting. However, no analysis of effort expectations was conducted, and given a very limited number of recruiters (only five participants working in the same company in Germany), these results could be biased or provide room for misinterpretation, as noted by  Laurim et al. (2021). Therefore, the findings of this manuscript can be considered as an extension and complement of the research of  Laurim et al.
Limitations and future research This research has several limitations which could be addressed in future research. First, the results were based on UTAUT's specific variables extended by frequency of actual AI use, which limits the generalizability to other factors that may predict recruiters' intention to use AI like satisfaction, confidence in technology, characteristics of the system (overall quality, processing speed or responsiveness of the system), security, ethical and legal issues in particular the General Data Protection Regulation in Europe or the Privacy Shield in the United States. Second, the perception of technology also depends on structural, economic, legal, cultural, and linguistic characteristics  (Panayotopoulou et al. , 2010; Strohmeier & Kabst, 2009). As argued by  Panayotopoulou et al.
Butterflies are a diverse and charismatic insect group that are thought to have evolved with plants and dispersed throughout the world in response to key geological events. However, these hypotheses have not been extensively tested because a comprehensive phylogenetic framework and datasets for butterfly larval hosts and global distributions are lacking. We sequenced 391 genes from nearly 2,300 butterfly species, sampled from 90 countries and 28 specimen collections, to reconstruct a new phylogenomic tree of butterflies representing 92% of all genera. Our phylogeny has strong support for nearly all nodes and demonstrates that at least 36 butterfly tribes require reclassification. Divergence time analyses imply an origin ~100 million years ago for butterflies and indicate that all but one family were present before the K/Pg extinction event.
We aggregated larval host datasets and global distribution records and found that butterflies are likely to have first fed on Fabaceae and originated in what is now the Americas. Soon after the Cretaceous Thermal Maximum, butterflies crossed Beringia and diversified in the Palaeotropics. Our results also reveal that most butterfly species are specialists that feed on only one larval host plant family. However, generalist butterflies that consume two or more plant families usually feed on closely related plants. Butterflies have long captivated naturalists, scientists and the public, and they have played a central part in studies of speciation, community ecology, plant-insect interactions, mimicry, genetics and conservation. Despite being the most intensely studied insect group, the evolutionary history and drivers of butterfly diversification remain poorly understood  1, 2.
Butterflies are thought to have diversified in relation to multiple abiotic and biotic factors, including adaptations to novel climates and species interactions, with caterpillar-host interactions and geographic history playing a major role  3. However, these hypotheses have not been tested because a robust phylogenetic framework at the taxonomic scale that would be needed to examine their evolution has not been available. Furthermore, host plant and distribution data have largely been scattered across literature, museum collections, and local databases, limiting our ability to conduct broad, comparative macroevolutionary studies. We sequenced 391 genes from nearly 2,300 butterfly species to reconstruct a new phylogenomic tree of butterflies representing 92% of all genera (Fig. 1  and Supplementary Fig.
1 ), assembled a comprehensive host association dataset and aggregated global distribution records. Using our tree, we inferred the evolutionary timing, patterns of host use, and biogeographic history of butterflies. We addressed three long-standing questions related to butterfly evolution: (1) did butterflies originate in the northern (Laurasia) or southern (Gondwana) hemisphere  4  ; (2) what plants did the ancestor of butterflies feed on  5  ; and (3) are host repertoires (that is, diets) of butterfly species and clades constrained by host phylogeny  6, 7  ? https://doi. org/10. 1038/s41559-023-02041-9 or translated amino acids with nine different subsets and partitioning schemes. Our trees were highly congruent, with strong support for the monophyly of all families and nearly all subfamilies with branch support metrics (SH-aLRT, ultrafast bootstrap) and multispecies coalescent species tree analyses (Supplementary Table  2 ).
Results and discussion To elucidate patterns of global butterfly diversification in space and time, we used targeted exon capture  8  to assemble a dataset of 391 gene regions (161,166 nucleotides and 53,722 amino acids) from 2,244 butterfly species (Supplementary Table  1 ). The majority (1,914 specimens) of butterflies sampled were newly sequenced for this study, representing all families, subfamilies and tribes, and 92% of recognized genera, from 90 countries. These were obtained from 28 specimen collections across the world (see section 2 of the Extended Online Methods). Phylogenomic trees were inferred with nucleotides https://doi. org/10. 1038/s41559-023-02041-9 We conducted 24 dating analyses using different fossil and secondary calibration schemes along with sensitivity analyses to assess the impact of analytical and sampling bias.
Most dispersal events between the Neotropics and the Nearctic took place after the Eocene/ Oligocene boundary, ~33. 9 Ma (Supplementary Fig. 4 ), congruent with a previous biogeographic study  15. Two lineages dispersed from the Eastern Palaearctic around 17 Ma, and these appear to be the first colonizers of Europe: ancestors of the Nymphalini subclade including Aglais, Nymphalis and Polygonia, and a clade of chequered skippers (Carcharodini; Supplementary Table  7 ). Butterflies were present on what are now all modern continental landmasses by the late Eocene (Supplementary Table  8 ). To understand the evolution of larval host plant use, we compiled 31,456 butterfly host records from 186 books, published papers, and public and private databases (Supplementary Table  9 ). We found that butterfly origin and diversification lagged far behind the origin of angiosperms  [16] [17] [18]  , corroborating previous studies  8, 19.
We used a recently developed network approach to create host plant modules to infer the associations of butterflies and plants  6, 20. Butterfly host plants include more than 80 orders and ~300 families  21  , rendering standard ancestral state reconstruction intractable. Our analyses provide support for Fabaceae as the larval host plant of the most recent common ancestor of butterflies (Supplementary Tables  10  and 11  and Supplementary Fig. 9 ), a widely accepted hypothesis  5  that has lacked empirical support. The crown age of the most recent common ancestor of Fabaceae is thought to be ~98 Ma (refs. 16,18), largely coincident with the origin of butterflies. Although most butterflies in our dataset are herbivores as larvae, a small number also feed on detritus, lichens or other insects (Supplementary Table  9 ).
The oldest associations in the entirely entomophagous Miletinae (Lycaenidae) appear to originate by 58. 4 Ma (58. 9-57. 1 Ma), a date that largely corresponds with an earlier estimation of the origin of this subfamily  22  (Supplementary Tables  4  and 12 ). Lycaenidae, with caterpillars that are ancestrally symbiotic with ants  8, 23  , date back to 64. 5 Ma (65. 4-63. 7 Ma) (Supplementary Fig. 10 ), long after the origin of ants (139-158 Ma)  24. Together with plants, ants appear to have provided a template for diversification of Lycaenidae and some members of its sister clade, Riodinidae. Our host database provides an important resource for future studies on butterfly feeding patterns. We examined host plant specificity on the butterfly phylogeny (Fig. 1 ) and found that more than two-thirds of extant butterfly species feed on a single plant family (67.
This finding supports the pattern proposed by Ehrlich and Raven  29  in which related butterflies feed on related plants. Fig. 3  | Relative mean dispersal rates of butterflies between bioregions. Numbers beside each arrow are average rates from 1,000 simulations using biogeographic stochastic mapping in BioGeoBEARS. These numbers were divided by 100 for ease of comparison (raw values can be found in Supplementary Data 5). E. , Eastern; W. , Western. https://doi. org/10. 1038/s41559-023-02041-9 Our study provides a robust baseline for future studies of this model insect lineage. The consistency of results obtained using different approaches for each of our analyses suggests that our conclusions are robust. Our data support the hypothesis that butterflies originated in the Americas in the late Cretaceous, 100 million years after the origin of angiosperms, and that they first fed on legumes.
Taxon sampling and sequence acquisition A total of 2,248 butterfly specimens representing 2,244 species in 1,644 genera were included for the molecular component of this study, along with ten outgroups from other lepidopteran superfamilies (Supplementary Table  1 ). The ingroup included genera from all families, subfamilies and tribes of butterflies according to the current classification. We aimed to include at least one species from every valid genus and sequenced the type species of each genus whenever possible. We obtained 92% of all described valid butterfly genera when the initial dataset was assembled ( July 2019). We obtained marker loci for phylogenetic analysis by (1) anchored hybrid enrichment exon capture of DNA extracts and subsequent Illumina sequencing  30  or (2) bioinformatically removing these sequences from published genomes and transcriptomes.
We successfully captured and sequenced DNA from decades-old museum specimens  37  , which enabled us to include taxa that are rare or live in areas where collecting fresh material is difficult. The oldest sample was a pinned specimen collected on 22 April 1946: Dira clytus (Nymphalidae) (LEP79391). Images of 460 representative voucher specimens are shown in Supplementary Data 2, and specimen repositories are listed in Supplementary Table  1. All voucher specimens, at minimum, had their wings and genitalia retained for identification and future research. We obtained sequence data from 343 published genomes and transcriptomes. Ten of these were outgroups representing nine moth families that are closely related to butterflies according to published studies on lepidopteran phylogeny  9, [38] [39] [40] [41] [42].
We extracted DNA from 1,915 specimens that were (1) stored in ethanol and frozen; (2) dried and stored in glassine envelopes under ambient conditions (papered); or (3) dried, spread and pinned in a museum collection. Locus assembly and sequence clean-up followed the pipeline of Breinholt et al. 42. Published sequences comprised (1) genome assemblies, (2) genomic reads, and (3) paired or (4) single-end transcriptomes. Three sequence datasets were created for this study: a nucleotide dataset with all codon positions (nt123); a nucleotide dataset that excludes all synonymous changes (degen), created using the Perl script Degen1 v. 1. 4 (refs. 43,44); and an amino acid (aa) dataset translated from the nt123 dataset (Supplementary Data 3).
Phylogenetic analysis and dating Maximum likelihood (ML) tree inference was conducted on all three datasets (nt123, degen and aa) in IQ-TREE 2. 0 (ref. 45); parameter settings for each analysis can be found in Supplementary Table  14. Branch support was calculated with 1,000 ultrafast bootstrap replicates (UFBS; '-B 1000' command)  46, 47  and Shimodaira-Hasegawa approximate likelihood ratio tests (SH-aLRT; '-alrt 1000' command)  48. Quartet sampling was performed on the degen359 and aa154 trees with the highest likelihood score. Four-cluster likelihood mapping analyses  49  were performed on the degen and aa datasets to assess the placement of particular butterfly clades that have been the subject of previous phylogenetic studies. We applied this approach in addition to standard branch support metrics, because the latter can be subject to inflated estimates  49.
Diversification rate analyses We performed a Bayesian analysis of macroevolutionary mixtures using the program BAMM v. 1. 10. 4 (ref. 57) to detect shifts in diversification rates between clades. Reversible-jump Markov chain Monte Carlo was run for 50 million generations and sampled every 50,000 generations. Priors were estimated with the R package BAMMtools v. 2. 1. 6 (ref. 58) using the command 'setBAMMpriors'. The tree was trimmed in Mesquite v. 3. 6 (ref. 59) to remove all outgroups. Six analyses were performed using different priors for expected numbers of shifts (5, 10, 20, 30, 40 and 50 shifts). We conducted a series of analyses in HiSSE (Hidden State Speciation and Extinction) and a BiSSE-like (Binary State Speciation and Extinction) implementation of HiSSE 60 in the R package hisse  61  to https://doi.
org/10. 1038/s41559-023-02041-9 evaluate whether there is a correlation between butterfly and plant diversification. We pruned outgroups from the aa154 dated tree (Strategy A) and compared 20 HiSSE models and BiSSE-like implementations of HiSSE. The BiSSE equivalent of HiSSE tests whether there are different diversification rates associated with the two host plant use states. Other models were built in the HiSSE framework to test alternative combinations of the presence or absence of hidden state and host plant use associations while also considering different transition rate matrices, net turnover rates, τi (speciation plus extinction: λi + μi) and extinction fractions, εi (extinction divided by speciation: μi/λi) (Supplementary Table  15 ). We tested whether diversification rates were linked to feeding (A) as a larval specialist or generalist (Supplementary Table  16 ); (B) on Poales (Supplementary Table  17 ) in Papilionoidea, Hesperiidae and Nymphalidae; (C) on Fabales (Supplementary Table  18 ) in Papilionoidea and Nymphalidae; (D) on Brassicales (Supplementary Table  19 ) in Papilionoidea and Pieridae; (E) on Fagales (Supplementary Table  20 ); (F) on the Poaceae module (Supplementary Table  21 ); (G) on the Fabaceae module (Supplementary Table  22 ); and (H) on Fabaceae in Eudaminae (Supplementary Tables  22  and 23 ).
We compared these different models of HiSSE and BiSSE-like implementations to account for hidden states to alleviate concerns that SSE models can lead to a high incidence of false positive results  62. Fraction files of clade-based taxonomic diversity estimates were created for all HiSSE runs to account for taxonomic sampling bias (Supplementary Table  24 ). We set the total number of extant butterfly species as 19,500, which is an ~8% increase compared with the butterfly species richness estimate of van Nieukerken et al. 63. We added this diversity correction based on many recent new butterfly species descriptions (for example, by Cong et al. 64  ) and morphospecies that we are aware of that have not yet been formally described. We estimated the total number of generalist and specialist species by calculating the percentage of generalists and specialists in our dataset at the family level.
We standardized the proportion of species richness in that family compared to all butterflies, based on diversity estimates of van Nieukerken et al. 63. For example, 78. 61% of all sampled Hesperiidae that had host data were specialists, and Hesperiidae comprise 21. 91% of all butterfly species richness; thus, we estimated Hesperiidae specialists as 19,500 × 0. 2191 × 0. 7861 = 3,359 species. Applying these calculations for all families yielded totals of 12,969 specialist species and 6,531 generalist species (Supplementary Table  25 ); these numbers were used to estimate fractions of generalists and specialists in our dataset. Calculating the fraction of species sampled within each host plant module proved more challenging. To estimate the true butterfly species richness for each module, we used unpublished estimates of species richness for all butterfly genera by G.