dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This research has been conducted within the  NEOROCKS  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 870403. CAO was supported by the  Australian Research Council (ARC)  through  Discovery  Project  DP190100252. SWC acknowledges support from the  National Research Foundation of Korea (NRF)  grant, No. 2020R1A2C3011091 , funded by the  Korea government (MSIT). We thank  J. Berthier ,  F. Spoto ,  M. Mahlke  for discussions related to the present article. The national facility capability for SkyMapper has been funded through  ARC LIEF  grant  LE130100104  from the  Australian Research Council , awarded to the  University of Sydney , the  Australian National University, Swinburne University of Technology , the  University of Queensland , the  University of Western Australia , the  University of Melbourne ,  Curtin University of Technology, Monash University  and the  Australian Astronomical Observatory.
SkyMapper is owned and operated by  The Australian National University's Research School of Astronomy and Astrophysics. The survey data were processed and provided by the  SkyMapper Team at ANU. The SkyMapper node of the  All-Sky Virtual Observatory (ASVO)  is hosted at the  National Computational Infrastructure (NCI). Development and support of the SkyMapper node of the ASVO has been funded in part by  Astronomy Australia Limited (AAL)  and the  Australian Government through the Commonwealth's Education Investment Fund (EIF)  and  National Collaborative Research Infrastructure Strategy (NCRIS) , particularly the  National eResearch Collaboration Tools and Resources (NeCTAR)  and the  Australian National Data Service Projects (ANDS). The Pan-STARRS1 Surveys ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
We present the Infrared Database of Extragalactic Observables from Spitzer (IDEOS), a homogeneous, publicly available, database of 77 fitted mid-infrared observables in the 5. 4-36 μm range, comprising measurements for 3335 galaxies observed in the low-resolution staring mode of the Infrared Spectrometer on board the Spitzer Space Telescope. Among the included observables are polycyclic aromatic hydrocarbon fluxes and their equivalent widths, the strength of the 9. 8 μm silicate feature, emission-line fluxes, solid-state features, rest-frame continuum fluxes, synthetic photometry, and a mid-infrared spectral classification. The IDEOS spectra were selected from the Cornell Atlas of Spitzer-IRS Sources. To our surprise, we have detected at a >95% confidence level crystalline silicates in the spectra of 786 IDEOS galaxies.
Introduction The successful mission of the Spitzer Space Telescope  (2003 -2020; Werner et al. 2004 ) has bestowed us with a rich scientific legacy comprising many thousands of publications. In many cases, the data that these papers are based on were extracted by researchers with extensive experience in Spitzer data processing. The individual calibrated frames obtained by the Spitzer instruments, or basic calibrated data (BCD) in Spitzer speak, are available from the Spitzer Heritage Archive 8  (SHA). This is also where the combined frames, extracted spectra, and celestial maps, the so-called post-BCD products, can be searched and downloaded from. To fill the gap between the bulk-processed products offered by the SHA and the level of processing that an instrument specialist can attain, the members of the Infrared Spectrograph (IRS;  Houck et al.
2004 ) team at Cornell University created CASSIS, the Cornell Atlas of Spitzer/ Infrared Spectrograph Sources 9   (Lebouteiller et al. 2011). For each IRS staring-mode observation, CASSIS offers the choice of several background-subtraction options and several spectral extraction methods, all of which can be visually compared to select and download the best spectrum for the source at hand. In 2012, we took the next logical step and started working on the Infrared Database of Extragalactic Observables from Spitzer (IDEOS). IDEOS builds on the publication-quality IRS spectra in the CASSIS repository by providing a homogeneous set of measurements of spectral features and continuum flux densities resulting from fitting and decomposing the IRS low-resolution spectra (R = 60-120) of a subset of 3335 extragalactic sources.
The selection of the IDEOS spectra and the preparation for their analysis are described in  Hernán-Caballero et al. (2016, hereafter  Paper I; see also Section 2). In the present paper (Paper II), we describe our fitting methods and present our observables. Technical details on the IRS modules and their characteristics can be found in  Houck et al. (2004). A large sample of homogeneously measured observables, like IDEOS, allows for the search for subtle trends that would remain elusive in smaller or heterogeneously measured data sets. In Section 9 we present an analysis of a number of weak absorption and emission features resulting from the presence of crystalline silicates in the interstellar medium (ISM) of IDEOS galaxies. Observations with the Infrared Space Observatory (ISO) in the 1990s have shown crystalline silicates to be a common component of interstellar dust in circumstellar environments  (Molster & Kemper 2005  and references therein).
Our paper is organized as follows. Section 2 provides a brief description of how we processed spectra selected from the CASSIS repository for ingestion in the IDEOS database. Section 3 describes in detail our method and assumption for fitting the IDEOS SEDs to obtain observables. Section 4 details how we deployed the spectral decomposition tools PAHFIT and QUESTFIT to obtain alternate sets of observables for our sample. Sections 5 and 6 describe how we compute rest-frame continuum fluxes and synthetic photometry, respectively. Section 7 explains our method for deriving silicate strengths. Section 8 discusses what can be learned from mid-infrared diagnostic plots containing thousands of IDEOS sources. Section 9 analyses the crystalline silicate features that we detected. Section 10 presents the IDEOS web portal.
In order to be able to measure observables from the IDEOS spectra, each extracted spectrum is first identified in the NASA Extragalactic Database (NED), is assigned a redshift, and has its spectral segments (orders) scaled and stitched. In addition, for 80 incomplete spectra, we were able to find and append additional spectral segments from other observations of the same target. A detailed description of these steps is provided in Paper I. We took great care defining meaningful fitting uncertainties for the IDEOS observables. It is important to realize, though, that these uncertainties do not include uncertainties associated with the flux calibration, slit losses, or stitching of the spectral segments. Uncertainties in the latter are compounded from segments LL1 to LL2 to SL1 to SL2, where we assume the scaling factor of the LL1 segment to be unity (see Paper I).
The segment-to-segment scaling uncertainties are generally small (<5%) but can be 10%-20% for the LL2 to SL1 scaling for low-signal-to-noise-ratio (S/N) spectra of semiextended sources, as it is hard to determine whether the flux in the small order overlap is affected by excessive noise and spurious features or not. The IDEOS portal (see Section 10) provides information about unusual stitching uncertainties as part of the stitching metadata for each spectrum. Among the 3558 IDEOS spectra, we found 110 targets that had multiple observations (380) of some or all spectral segments (SL2/SL1/LL2/LL1). For 25 of these targets, it was beneficial to the S/N of the spectrum to average these observations. Among them are Arp 220, IRAS 08572 +3915NW, 3C 273, IRAS 07598+6508, and Mrk 231. For the remaining targets, we identified the spectrum with the best S/N and use that spectrum in scatter plots as well as in statistical computations in this paper.
Spectral Fitting We have used the Levenberg-Marquardt least-squares fitting software package MPFIT  (Markwardt 2009)  and empirical spectral templates to measure fluxes of emission features, optical depths of absorption features, and flux densities of the continuum in six rest-frame ranges of the mid-infrared spectrum. The ranges are modeled independently from one another, without any prior assumptions on the underlying continuum other than it being a polynomial of a predefined order. The latter choice was made to ensure the best possible fit to the individual features. We will refer to this approach as "chunk fitting," and the code that implements it as "CHUNKFIT. " The features fitted in the six ranges comprise various polycyclic aromatic hydrocarbon (PAH) emission bands, finestructure lines, pure rotational molecular hydrogen lines, crystalline silicate features, and absorption bands of water ice and aliphatic hydrocarbons.
In practice, however, MPFIT fits a higher fraction of the lines present when the line center is allowed to shift by ±600 km s -1 and the line width is allowed to vary by ±10%. Note that at the low spectral resolving power of the IRS low-resolution modules, line asymmetries and line broadening associated with ionized gas outflows in active galactic nuclei (AGNs)  (Dasyra et al. 2008)  and ULIRGs  (Spoon et al. 2009 ) cannot be resolved. It therefore is safe to assume that all emission lines have Gaussian line profiles. For PAH features, we use a different approach to limit false detections. Because a strong detection of an intrinsically weak PAH band likely indicates that a noise feature was fitted, we constrain the allowed ratio of a faint to a strong PAH feature to an empirically determined range measured from a sample of high-S/N PAH spectra.
MPFIT does not compute upper limits for spectral features it deems unnecessary to use in a fit, nor does it check whether features that it does fit constitute true detections. Upon completion of a CHUNKFIT model, we therefore impose upper limits on a spectral feature if the height of that feature is less than three times the rms noise divided by the square root of the number of spectral data points within the full width of the feature at 70% of the peak flux (FW70). The latter factor lowers the threshold for PAH features to be deemed detected, as the FW70 of a PAH feature is higher than that for an emission line. A complication in this effort arises from the fact that for most CASSIS spectra, the formal rms uncertainty of the spectra is overestimated by up to a factor of 3 when compared to the dispersion of flux values between nearby pixels in continuum regions of the spectrum.
Silicate Features in Spectra of Centrally Heated Dust Geometries The above observations are consistent with an origin for the amorphous and crystalline silicate features in a centrally heated dust geometry, either spherical in nature or arranged in a torus geometry. In Figure  22  we show simulated 5-40 μm spectra for five centrally heated dust shells with dust masses increasing by a factor of 100 from the top to the bottom spectra. The model spectra have been computed using the 2D version of the MCMAX code  (Min et al. 2009) , with the following parameters. For the central heating source, we adopt a 2500 K stellar SED. The gas density falls off as r -1 and is parameterized as in  Meixner et al. (2002). The dust mixture is composed of 75% MgFeSiO 4 amorphous dust (Jena database optical constants;  Dorschner et al.
So, we can observe the emergence of an improvised relationship based on gestures that are intelligible and sensitive and that demonstrate how programming tools can also create aesthetic experiences. forms of the illusion between reality and virtuality (Guez, 2015b) through a series of various experiments where virtual entities that can be realistic or imaginary are used. It was through the CIGALE Project that I was able to delve into these questions with the INREV-AIAC team and the CNRS Formal Structures of Language Laboratory at the University of Paris-8 (Guez et al. , 2013). This project on capturing artistic, linguistic, and expressive gestures, allowed us to make the installation, InterACTE 8 where a virtual actor adapts their gestures while facing a spectator. Through real-time interaction, they are also able to make new gestures thanks to our behaviour engine (Batras et al.
This autonomy gives it the ability to behave like a living creature by simulating human perceptive, sensory and motor functions that have been incorporated into its artificial bodies. So, the gestural dialogue between human actors and virtual actors gives the impression that a lively, rich and complex relationship exists-one that is receptive to the unexpected, which is conducive to aesthetic experiences. The virtual tightrope walker is part of this body of work with its ability to invent rebalancing strategies as it interacts with the spectator. More concretely, the exteriorisation of human gestures and their transfer to virtual humans can be done in various ways. They can take place in real time through movement capture (MOCAP) where the trajectory of the spectator's gestures interacts with the digital installation, or in a delayed manner where the gestures of human actors are pre-recorded in a MOCAP database so that they can be used later.
Classifications used in analyses and their rationale are explained in more detail in the supplementary material. Our final dataset included 3175 species with nest information (68% of all Passeri). Given that response variables like range size can be associated with body size and species latitude, we also collected information on body size for each of the species from  Wilman et al. (2014) , who collated this information mainly  from Dunning Jr (2007) ; and information on the mean latitude from their distribution range from  Sheard et al. (2020). As a measure of colonisation of novel habitats, we also collated information of species' presence in urban environments, which was extracted from the IUCN habitat scheme (data from IUCN, collated in  Ducatez et al. (2020) ). This was recorded as 1 (presence in urban areas) or 0 (no presence).
Environmental variables To test whether there are differences in niche width and range size between species with different nest types, we used geographical distribution information from BirdLife  International and HBW (2019). We used a published dataset  (Cally et al. , 2021)  of bioclimatic variables from Worldclim  (Fick & Hijmans, 2017 ) (details of extraction in  Cally et al. , 2021, and 14  variables used in Table  S1 ). These variables were sampled in 1000 random points across the distribution of each species, and provide information on temperature and rainfall across the range. We also extracted information on range size from  Cally et al. (2021)  (n = 3175), who used shapefiles from Birdlife (BirdLife International and HBW, 2019). For a smaller set of species for which breeding range information was available (n = 3049), we extracted information on the same 14 bioclimatic variables following the protocol in  Cally et al.
(2021) , but this time we used a more recently developed dataset (CHELSA) resulting from an algorithm that predicts current precipitation patterns at high spatial resolution  (Karger & Zimmermann, 2019). For each climatic variable, and each species, we calculated the standard deviation across the 1000 points sampled, to estimate climatic variability across the species range (and breeding range). Since species restricted to islands are limited both in the extent of their range and the niche width, we performed analyses using both the whole dataset and only continental species. Finally, we also performed additional analyses on elevational ranges of species to test whether wider elevational rangesassociated with higher climatic variation-are linked with nest type. Details of this approach can be found in the supplementary material.
Tip-level regression analyses Information on days taken to build a nest was available for 277 species in HBW (69 domed, 208 open), and to test whether there are differences in the time taken to build a nest we used linear models that account for evolutionary history (PGLS, description of approach below). We used the time taken to build in days as response variable (log transformed) and we used log body size, the sex of the builder (female, male or both), latitude (absolute) and the type of nest built (domed/open) as predictors. We provide details on the data employed for this analysis in the supplementary material. To test whether building different nest types is associated with current range size and niche width, we used linear models that account for evolutionary history, with climatic niche width (PC TEMP or PC PRE ), range size or presence in urban environments (yes/no) as response variables.
We used the R package 'performance' and the command check_model to detect outliers, and assess whether there were any collinearity issues in our set of predictors  (Lüdecke et al. , 2019). For the continuous response variables (time spent building nest, PC TEMP , PC PRE and range size-breeding and whole), we used a phylogenetic generalised least squares regression (PGLS), using maximum likelihood to estimate lambda, implemented in the R package 'caper'  (Orme, 2013). To control for phylogenetic relatedness among species, we obtained a maximum clade credibility tree (MCC, across 10,000 trees) using the package 'Phangorn'  (Schliep, 2011)  and a set of 10,000 phylogenies from birdtree. org (Jetz et al. , 2012). For models with significant results using the MCC tree, we also performed PGLS analyses across a set of 100 trees.
For each model using the MCC tree, we report the estimates and p-values calculated, and for the analysis on 100 trees we generated 95% highest posterior density intervals (HPD) for the estimates using the R package 'coda'  (Plummer et al. , 2006). For the binary response variable (urban or not), we used a Bayesian approach in the R package MCMCglmm  (Hadfield, 2010)  using nest type (open, domed, cavity) and log body size as predictors. We run one model using the MCC tree as a random effect until convergence was reached. To account for phylogenetic uncertainty, we followed  Ross et al. (2013). Briefly, we run the model using 1300 different trees and for each tree used 100,000 iterations and saved the last iteration before going into the next tree. We used the first 300 saved iterations (e.
Diversification analyses and evolutionary models We estimated speciation rates across a range of phylogenetic trees using the cladogenetic diversification rate shift (ClaDS) Bayesian model  (Maliet et al. , 2019)  that infers branch-specific diversification rates. ClaDS allows for gradual variation in diversification rates and was shown to perform well in identifying both small and large rate shifts  (Maliet et al. , 2019). We used the model that accounts for a scenario with a constant turnover rate (ClaDS2 model) and the faster implementation in Julia (v. 1. 1. 0, Bezanson et al. , 2017)  that uses data augmentation  (Maliet & Morlon, 2020) , setting the sampling probability to 0. 63. To test whether nest type is associated with variation in speciation rates, we used the tip-rate estimates (estimates for each terminal branch at present) extracted from ClaDS as response variable in a PGLS model, with nest type and log(range size) as predictors, since range size has been shown to be associated with speciation  (Cally et al.
, 2007). We modelled the correlated evolution of niche width and nest type data by aggregating them into four different states: narrow niche and domed nest, narrow niche and open nest, wide niche and domed nest and wide niche and open nest. We assigned a species as having a wide or narrow niche based on whether the PC TEMP was below or above the median value of the whole dataset (PC TEMP = -0. 639). Species with narrow niches also had smaller ranges and were more likely to be tropical. Since we are interested in the interaction among these states, we disallowed transition rates between states that effectively would represent simultaneous changes in nest type and niche width (e. g. going from wide niche and domed nest directly to narrow niche and open nest is not allowed). Finally, to examine whether nest type provides a mechanistic transition route between niche breadths, we performed a direct test on the evolutionary association between nest type and niche width category (same binary classification as described above), using the software BayesTraits (V3.
, 2016) , including ancestral state estimation (details in supplementary material). To evaluate the effect of phylogenetic uncertainty, we ran the Maximum Likelihood approach MuSSE implemented in the R package 'castor'  (Louca & Doebeli, 2018)  across 100 phylogenetic trees. Because results were consistent across methods, we present the castor analyses in the main text and RevBayes (MuSSE and HiSSE) results in the supplementary material. Finally, given that the uncertainty of species position in the phylogeny might be higher for some states, potentially biasing transition rates, we implemented an additional analysis that specifically accounted for missing data per character state (details in supplementary material). All analyses were carried out in R except where specified that another software was employed (V4.
003], t-value = -1. 51, p = 0. 13) or who builds the nest (β = 0. 14 [0. 14, 0. 16], t-value = 1. 63, p = 0. 10). Species with domed nests tend to have a tropical distribution, but there was no link between nest type and body size in our dataset (Figure  S2 ), so there was enough variation in these predictors across species to avoid multicollinearity issues, and all variance inflation factors were low (max 2. 18), and well below the recommended threshold of five  (Gareth et al. , 2013). The area of geographic and breeding ranges was predicted by nest type, and species with domed nests had significantly smaller ranges compared to both, species that build open nests and those that use cavities, after controlling for the significant effects of latitude and body size on range size (Figure  3 ).
Our analyses also suggest that clades with open nests might present lower extinction rates compared to those that build domed nests, offering additional support to the idea of open nests as important in the passerine radiation. We found differences in extinction rates that are consistent across trees, analyses, and linked specifically to nest type. Dome-nesting species (with either small or wide niches/ranges) tend to have higher extinction rates compared to species with open nests. This link cannot be explained through relationships between body size and nest type, since dome-nesting species tend to be smaller and smaller species have been found to have lower (not higher) extinction rates  (Bennett & Owens, 1997; Gaston & Blackburn, 1995). We, however, strongly highlight that estimates of extinction rates from molecular phylogenies should be taken with extreme caution  (Rabosky, 2010)  and differences in rates were small, had high variation and were even smaller when the dataset used included only species with genetic data.