dataesr/scientific-paragraphs-categorization huggingface dataset:
We did not consider species left in open nomenclature, except in some rare cases where specimens clearly represented new taxa that were not formally described in the literature (Supplementary Data S6). Each occurrence represents the report of a given fossil species in the literature with unique geographic and stratigraphic origins. Therefore, we did not consider fossil occurrences that are geographically and stratigraphically similar to others already considered (fossils sampled from the same geological horizon in similar locality). This avoids artificially inflating the number of occurrences based on specimens that possibly belong to the same individual or population  52. Our updated database includes 1397 fossil occurrences of 324 species (Supplementary Data S7), of which 37 are extant species (and 35 have available molecular data).
Estimates of fossil occurrence-based diversification rates. We performed Bayesian inferences of the fossil occurrence data under a birth-death model with constrained shifts (BDCS  78  ), here referred to as 'BDCS-Fossils'. The BDCS model estimates the parameters of the preservation process, the times of speciation (Ts, i. e. species origination) and extinction (Te, i. e. species extinction) of each species, the speciation (λ) and extinction (μ) rates and their variation through time over specific time bins. This method uses all occurences of each fossil species to estimate Ts and Te, which can be regarded as estimated lifespans of species, while taking into account preservation biases  23. Hence, species are considered present from their estimated time of speciation until their estimated time of extinction, which produces no Lazarus taxa.
We estimated the diversification dynamics of carcharhiniforms by adopting an inclusive approach based on both fossil record and phylogenetic data. Combining the fossil occurrence-based and phylogeny-based Ts-Te (Fig. 3a, b ) yielded a global dataset including 483 carcharhiniform species in total (Fig. 3c ), and represented by (1) 324 species for which the Ts-Te are estimated with the fossil record (287 extinct and 37 extant species, of which 35 are sampled in the phylogeny) and (  2 ) 159 species for which the Ts-Te are estimated with the phylogeny. The combined Ts-Te dataset was used to compute a LTT plot based on range-through diversity with the -ltt 1 option in PyRate (Fig. 3d ). We performed PyRate analyses over the combined dataset using the BDCS model (here referred to as 'BDCS-Combined') to estimate speciation, extinction, and net diversification rates (Fig.
We ran PyRate for 20 million of MCMC generations estimating speciation and extinction rates to be constant within specified time bins (-fixShift option) of 10 Myrs (main analysis) or corresponding to geological epochs (sensitivity analysis). Chain mixing and ESS were examined in Tracer 1. 7. 1  67  after excluding the first 10% of the samples as burn-in. We then combined the posterior estimates of the speciation and extinction rates across all replicates to generate rates-through-time plots. Tooth crown height data and size classes. Carcharhiniforms gather species with a wide range of sizes and ecologies, and may not show a homogeneous evolutionary history. We used tooth crown size as a proxy for body length and therefore feeding ecological niche (e. g. large teeth correspond to species of high trophic level) based on data gathered in Condamine et al.
Tooth measurements for extant carcharhiniform species were made for 220 species (77. 46%) based on specimens present in the collections of the University of Montpellier or on tooth illustrations in the literature. Crown height values of the 64 remaining extant species were estimated as follows: (1) total body lengths of extant carcharhiniform species were compiled from FishBase  80  and the literature (Supplementary Data S8); (2) linear regressions of body length versus tooth size were performed for each family, which were all statistically significant (Supplementary Data S9); and (3) equations of linear regressions were used to determine the tooth sizes for extant species with missing data (Supplementary Data S9). All extinct and extant species were assigned to one of the three size classes ("small", "medium" and "large") following the cut-off values provided by Condamine et al.
The same rationale applies for the extinction rates, μ(t). We ran the MBD model on the combined Ts-Te dataset using 20 million MCMC iterations and sampling every 20,000 to approximate the posterior distribution of all parameters (λ0, μ0, four Gλ, four Gμ, and the shrinkage weights of each correlation parameter, ω). We set the -rmDD option to 1 in the MBD analyses on the global (order-level) dataset because testing for diversity-dependence in the entire carcharhiniform dataset would not be meaningful considering the wide range of ecologies represented in this order (see above). However, we tested for the effect of diversity-dependence in the MBD analyses per size classes (-rmDD 0 option). To identify significant correlations, previous analyses  53  have relied solely on ω > 0. 5, whereas others 52,82 adopted more conservative selection criteria where correlations were considered strongly significant only when fulfilling the double condition ω > 0.
We therefore compiled data on biological reef volume variations, i. e. including volume data of rocks bio-constructed by metazoan organisms (e. g. bivalves, sponges, cnidarians, foraminifera), produced by Kiessling and Simpson  93. Sea-level and δ  13  C data were subsampled every 0. 5 Myrs using the smooth. spline function in the R-package pspline 1. 0-18  94  with a degree of freedom of 200. All the data are made available (Supplementary Data S10). Among the five selected variables, it could be expected that some covary, especially global temperatures and sea level. However, sea level variations are not only due to temperature change but also due to ice volume, tectonic activity, Milankovitch cycles, and ocean basin dynamics  92, 95. Consequently, most previous macroevolutionary studies that tested the links between these variables and past biodiversity fluctuations found different relationships according to the tested variables  7, 52, 85.
Diversification and diversity dynamics through time. The fossil record of carcharhiniforms includes 1,397 occurrences assigned at the species level, which represent 324 species (37 extant and 287 extinct). We inferred the diversification history of carcharhiniforms using fossil data only through a birth-death model (BDCS-Fossils). Shifts were analysed using predefined time intervals (10 Myrs or geological epochs), including or not the -N parameter informing the number of extant species, where rates can change between time bins (Supplementary Data S12-S13). Results of the BDCS-Fossils analyses without the -N option provided similar rate results as the analysis using the -N option (Supplementary Data S13), indicating no effect of this option in correcting for missing extant species in the fossil occurrences dataset.
5 ; Supplementary Data S15). Combined data indicate two periods of negative net diversification rates at 70-80 Ma and at the EOT, both due to a joint effect of higher extinction and lower speciation rates. However, diversification patterns including neontological data diverge from those based on fossil data only over the last 20 Myrs (even when the diversity through time is log-transformed; Supplementary Data S16). Combined data indicate a speciation burst from the early Miocene onward, which is in line with the high Oligocene speciation rate, while fossil data indicate decreasing speciation rates, which results in negative net diversification in the last 10 Myrs (Supplementary Data S16). We also estimated the diversification dynamics of three carcharhiniform classes (based on tooth size akin to ecological niches) with the combined fossil and phylogenetic dataset under the BDCS model (shifts every 10 Myrs, Supplementary Data S17).
Tree-based approaches probably represent future avenues of research in macroevolution, but other approaches that break free from a total-evidence framework should be considered. We studied the most speciose shark order, for which a total-evidence dating would be very challenging as no matrix gathering morphological characters can be used for integrating fossils in an extant time-tree. Such a dataset is extremely complicated to assemble because of the high diversity of dental forms that are probably related to the broad ecological habits and feeding strategies, which makes the definition of shared morphological characters and identification of homologous structures difficult. Therefore, we have built an approach relying on.
Paleogene Neogene Cretaceous Figure  6. Estimates of species lifespan from the fossil record and the molecular phylogeny for the clade gathering Galeocerdidae, Sphyrnidae, and Carcharhinidae. Species lifespan is determined by the times of speciation (Ts) and times of extinction (Te), as inferred with the fossil record for extinct species (yellow) and extant species having fossil occurrences (orange), and those inferred with the dated phylogeny for extant species only (blue). Summing up the number of species per million-year bins allows recovering the diversity dynamics of the clade through time. See Supplementary Data S14 for data on the entire Carcharhiniformes. both the fossil occurrences and a time-calibrated molecular phylogeny to estimate times of speciation (Ts) and of extinction (Te) from the fossil data for both extinct and extant species (for those that have a fossil record), and Ts from the phylogeny of extant species (Te for extant species being equal to zero).
These Ts and Te were then combined in a single dataset analysed with PyRate using only the Ts and Te  53, 78. This methodology has some advantages for macroevolutionary studies. First, this is relatively easy to implement, although there are still some difficulties in assembling a molecular phylogenetic dataset along with a fossil dataset at the same taxonomic level. Nonetheless, time-calibrated phylogenies are becoming increasingly popular, even for non-model clades, and there are online fossil databases (e. g. Paleobiology Database) that gather a massive number of fossil occurrences for some groups (e. g. mammals, bivalves). Second, our approach is particularly appropriate for clades that experienced recent radiations but lack a fossil record for most living species, like shown with the Carcharhiniformes.
This is in line with the globally increasing taxonomic diversity of these ecological groups in the recent past, especially small carcharhiniforms, and is supported by evidence of niche partitioning observed in living members of this order  [114] [115] [116]. Our study combining independent datasets is based on a few assumptions and can produce several limitations. Our approach relies on phylogeny-based and fossil occurrence-based estimates of species' lifespans (Ts-Te), which are based on a number of well-known model assumptions pertaining to molecular relaxed clocks  64, 71  as well as for PyRate birth-death models  23, 24. In particular, the latter models assume that (1) speciation to occur by budding, bifurcating and anagenesis; (2) extinction occurs at the end of a taxon lifespan and can happen following anagenetic and bifurcating speciation events (in these cases, the ancestral species goes extinct), (3) both speciation and extinction rates are homogeneous across lineages; (4) preservation can vary through time; (5)  preservation is heterogeneous across taxa.
In our case, the BDC model supports compatible rates of diversification between fossil and phylogenetic datasets, which indicates that the patterns of diversification provided by the combination of both datasets are congruent (Supplementary Data S20). Therefore, although the sampling fraction between phylogeny and fossil record data are probably heterogeneous, they provide comparable diversification patterns. Despite these limitations, this approach is relevant for clades with a poor fossil record in recent times combined with recent diversification events that are not entirely captured by the fossil record. Although methods for correcting Ts-Te of sampled taxa have been developed, one of the main challenges in palaeodiversity estimates is the inference of non-sampled taxa  33, 122. This is particularly problematic for clades like Carcharhiniformes, for which lineages from some environments (deep-sea) and periods of time (Oligocene, upper Miocene-Recent) are weakly sampled.
The comparison between Ts estimated with fossils and phylogeny for the 35 extant species represented in both data (Supplementary Data S5) shows a substantial number of instances (21/35) where the phylogeny produces slightly older ages than the fossil record, even for well-sampled clades (e. g. Carcharhinus). It is expected that phylogenetic estimates produce older Ts than fossil occurrence-based estimates because Ts from phylogenies do not exactly reflect speciation age, but instead divergence time from the sister species (Fig. 3 ). Furthermore, the mean difference between Ts estimated from the phylogeny and Ts from the fossil record is weak and positive (5. 67 Ma) and even weaker (2. 33 Ma) when two outliers are removed (Supplementary Data S5). Consequently, adding Ts data of living species extracted from the phylogeny does not necessarily mean adding young species in the dataset, and the pattern of recent diversification burst observed in carcharhiniforms is likely realistic and in line with the fossil occurrence-based diversification observed prior to the Miocene and with the observed diversity of living species.
Conclusions We estimated deep-time diversity dynamics by combining species-level palaeontological and neontological data in a Bayesian framework. Taking the carcharhiniform sharks as a model group, we estimated times of speciation and extinction from fossil occurrences analysed through process-based birth-death models, which were complemented by times of speciation from time-calibrated molecular phylogenies for living species that are not represented in the fossil record. Bayesian inferences of the combined dataset yielded estimates of variations in speciation and extinction rates as well as diversity fluctuations over the entire clade's evolutionary history. Our approach supports (1) a complex evolutionary history exemplified by the numerous variations in diversification rates through the ~ 180 Ma of the clade's lifespan with an early low diversity period followed by a radiation exacerbated since 30 Ma, and (2) the role of reef expansion and temperature change to explain such variations in diversification through time.
The divergence times provide Ts for extant species that have no fossil record (see Fig. 3for details on this step). We then combined the Ts and Te estimated with PyRate and the Ts and Te inferred with molecular dating in BEAST to generate the most comprehensive species-level dataset incorporating Ts and Te as well as the age uncertainties. This dataset is used for the subsequent analyses under the BDCS model (BDCS-Combined) to compare diversification dynamics with the fossil-only estimates, and the multivariate birth-death (MBD) model to assess the role of global environmental change, as well as clade competition on speciation and extinction rates.
Data collection and initial processing Participants were followed for 4 continuous days from day 1 (3 am) to day 5 (3 am) with a MicroAethalometer (MicroAeth AE51, AethLabs, CA, USA). Personally measured concentrations of BC around the breathing zone was continuously measured at a 10 s time resolution with the AE51 strapped on participants' shoulder with the inlet of the tube at the level of neck. AE51 is a valid device  (Cai et al. , 2014)  that has already been used in multiple studies  (Morales Betancourt et al. , 2017; de Nazelle et al. , 2012; Apte et al. , 2011; Cheng and Lin, 2013). This pocket-sized device analyses BC mass by examining the changes in absorption of transmitted light [coefficient of light attenuation (ATN)] at 880 nm on a filter strip housing a small-sized Teflon-coated borosilicate glass fibre filter (T60).
BC concentration measured by AE51 can be substantially underestimated when the filter is overloaded. Thus, to avoid filter saturation and preserve measurement integrity, participants were asked to change the filter on the second day at 8 pm when they were recruited in winter, and the filter was changed in any case by a new one whenever the device indicated necessity of filter replacement (when the optical density reached a certain level). Therefore, data do not need to be corrected for any sort of filter loading effects  (Correia et al. , 2020). The post processing of BC data was done using the Optimized Noise Reduction Averaging (ONA) algorithm  (Hagler et al. , 2011)  (see details of the ONA algorithm in Appendix 2). We provide our version of the algorithm in the R package "BlackCarbon" version 1.
Calculation of minute ventilation and inhaled doses Dons et al in 2017 published a paper comparing several methods for estimating minute ventilation and made recommendations for using those based on the objective, duration, and the size of the study  (Dons et al. , 2017). Following the paper, different methods appropriate to our data and study type were chosen to estimate minute ventilation as specified in Appendix 3 (Appendix Table  A2 ). Since accelerometer data were collected throughout the study, they allowed us to include a larger number of trips stages with minute ventilation than when breathing rate and heat rate measurements recorded only on the 2nd and 4th days were used. Therefore, method 1. 1, using METs (Appendix Table  A2 ), was chosen to report inhaled doses in the main text.
Classification of trip stages Trip stages are segments of trips with a unique transport mode used. Successive trip stages within a trip are necessarily separated by a time segment of mode transfer assigned to a stationary location. After completion of the sensor data collection, data extracted from the BT-Q1000XT GPS receiver were uploaded in the TripBuilder web mapping application  (Oliveira et al. , 2011; Wolf et al. , 2004 ) that preprocessed them to identify the start and end times of each trip stage and related transport modes (see detail in Appendix 4). Using the TripBuilder application, the trip stage data over the whole period (including trip stage start and end and mode) were verified with the participants during a phone mobility survey. The final output is a timetable including time-stamped information on trip stages and related information.
Estimating porous media properties is a vital component of geosciences and the physics of porous media. Until now, imaging techniques have focused on methodologies to match image-derived flows or geomechanical parameters with experimentally identified values. Less emphasis has been placed on the compromise between image processing techniques and the consequences on topological and morphological characteristics and on computed properties such as permeability. The effects of some of the most popular image processing techniques (filtering and segmentation) available in open source on 3D X-ray Microscopy (micro-XRM) images are qualitatively and quantitatively discussed. We observe the impacts of various filters such as erosion-dilation and compare the efficiency of Otsu's method of thresholding and the machine-learning-based software Ilastik for segmentation.
The entire set-up and methodology were previously described by Voltolini et al. [19]. For the two-phase flow of scCO 2 , the images were collected on the core plug, and experiments were successively performed on the same sample in the same position. The complete dataset is a stack of 853 slices of 2650 × 2414 with a voxel size of 4. 4 µm. Images were cropped to 800 × 800 × 800 voxels and were processed using a fully assembled computer with a 12-core Intel ® Core™ i7-5820K CPU @ 3. 30 GHz, with 32 Gigabytes of RAM and an NVIDIA GM204 GPU under Ubuntu 18. 04. 6.
Machine learning software [20] is used to extend the number of identification criteria in the same way as the human brain. For anyone who has processed images obtained by 3D X-ray tomography, finding the best filter during the processing phases by playing with the associated tuning parameters and reiterating the approach for thresholding is subjective, leading to human inferences in the choice of the best compromise. The possibility of obtaining a correct segmentation of standard processing is therefore limited to some very distinct gray levels between phases. In the sample chosen, for example, fluids are associated with very smooth shapes and macroscopic curvatures, representing criteria additional to contrast and gray levels during the pixel classification phase. For example, the gray levels of doped water and smectites are almost identical, and only Ilastik or equivalent tools are able to separate objects with peak overlapping.
Automation of the entire image processing workflow in a deep learning process is therefore not yet mature but it could be quite interesting for reducing image processing time and controlling errors in images. Ilastik was selected for its efficiency compared to that of the software generally used in this field. Ilastik is open-source software, which in a preliminary phase demonstrated highly effective image labeling without ever producing confusing results, whatever the gray levels. Of course, there are many more advanced filtering and segmentation algorithms, and it is impossible to list them all. There are some very promising image-quality enhancers, filters, and segmentation algorithms, but they remain a niche technique in a field of application that generally uses basic methods. Among them, we could mention the Hybrid no-reference quality metric for singly and multiply distorted images, A fast reliable image quality predictor by fusing 2 micro-and macro-structures, filtering structured artefacts using suitable algorithms and segmentation generated using certain semantic segmentation methods, Image Segmentation by Data-Driven Markov Chain Monte Carlo, and many others.
On the other hand, automatic thresholding involves automatically selecting one or more optimal gray-level threshold values for separating objects, which is the premise of Otsu's method. The algorithm performs thresholding slice by slice, with the main advantage being that it considers the source drift, and the spectral and spatial characteristics of off-focal radiation. With the large number of free libraries available, and the increase in computing capacities, Artificial Intelligence has given rise a new generation of image processing tools such as Weka  [24]  and Ilastik  [25, 26]. We investigated these free, open-source software programs and compared them to the more conventional workflows involving pre-filtering and segmentation. The Weka (Waikato Environment for Knowledge Analysis  [24] ) classifier for 2D and 3D images, which combines a series of machine learning algorithms with a set of selected image features, proved quite efficient  [27].
Methodology The effects of the previously described filters were compared by applying overlapping masks on the raw image to locally identify tendencies in a specific part of the 3D dataset, and by performing segmentation using Otsu's method to extract the pore space. Binarization was necessary to numerically determine the morphological and topological properties strongly impacting flow in porous media. We decided to perform segmentation using multi-Otsu thresholding, which is based on intensity histograms, and pixel intensity classification is the basic characteristic used in image filtering. Our goal during observation was therefore to determine whether or not certain additional elements would significantly increase image quality. Please note that Artificial Intelligence methods can be used on raw images, meaning that they can be used to correct noise reduction and certain types of artifacts due to the large number of classifiers.