dataesr/scientific-paragraphs-categorization huggingface dataset:
In his reply to Lucas' comments  (Lucas, 2013) ,  Benton (2013:e294)  summarized the relevant issues: "He  [Lucas]  says that the uppermost tetrapod-bearing terrestrial Permian beds in Oklahoma and Texas (United States) are Early Permian in age, and that the oldest Russian tetrapod-bearing beds are post-Roadian, so leaving a temporal gap of 2-3 m. y. On the first point, his view is not accepted by the majority of researchers, although the biostratigraphic evidence is far from watertight, and on the second, he is opposed by the majority of experts. " We agree with Benton and have further substantiated his conclusions in the current paper. Most recently,  Brocklehurst (2020)  used tip dating in a novel way to assess the age of these formations. His analyses relied on three previously published datasets: one on Paleozoic amniotes  (Ford and Benson, 2020) , one on caseids  (Romano et al.
, 2017) , and one on captorhinids  (Modesto et al. , 2018a). Normally, tip dating is used to infer topology and branch lengths (hence, nodal ages, too) from a dataset consisting of character data and geological ages of extinct taxa. Because the latter can incorporate uncertainty, Brocklehurst (2020: 3) indicated that "age priors were represented by a uniform distribution between the lowermost and uppermost age of each formation". However, instead of using a single set of age priors as is typically done, he considered three sets of age priors, which differ by the assumed age of the San Angelo and Chickasha Formations. One set, called "extinction hypothesis", placed these formations in the early Roadian with their base at the Kungurian/Roadian boundary and with the top of the Chickasha extending higher than the San Angelo, as it does in all three sets.
The second set, called "gap A hypothesis", placed these formations in the late Kungurian with the top of the Chickasha Formation extending to the Kungurian/Roadian boundary; this matches the ages suggested by  Lucas until recently (e. g. , Lucas 2002b , 2004 , 2018: fig. 8 ). The third set, called "gap B hypothesis", placed the San Angelo and Chickasha formations at the bottom of the Kungurian with their bases at the Artinskian/Kungurian boundary; this matches the ages suggested recently by  Lucas and Golubev (2019)  and accepted by  Schneider et al. (2020). Brocklehurst (2020)  then performed three sets of tip-dating analyses on the three datasets, one for each set of age priors. He used Bayes factors  (Kass and Raftery, 1995)  to evaluate relative support for each set of age priors. Bayes factor is a measure of relative support and is typically used to compare support for two models, much like the classical log-likelihood ratio test  (Woolf, 1957) , but the Bayes factor differs from the latter by integrating over all possible parameter values of the compared models, whereas the loglikelihood ratio compares two models with sets of estimated parameter values.
Unsurprisingly, the hypothesis best supported by Bayes factors is overwhelmingly the "extinction hypothesis" that assumes a Roadian age for both formations. Indeed, for the amniote dataset, the Bayes factor of the extinction hypothesis compared to the gap A hypothesis is 244, and that of the extinction hypothesis compared to the gap B hypothesis is 580. It is generally considered that the evidence in favor of a model over an alternative one is substantial when the Bayes factor is 3 to 20, strong for values of 20 to 150, and decisive for values >150  (Kass and Raftery, 1995: 777). The other two datasets appear to offer less discrimination between hypotheses, but they still favor strongly the extinction hypothesis, with Bayes factors ranging from 26 to 106  (Brocklehurst, 2020: table 1). Thus, taken together, tip dating of these three datasets provides decisive support for a Roadian age of the San Angelo and Chickasha formations.
Conclusions The evidence described in this review leads to the following conclusions: 1. Lithostratigraphic data show that the vertebrate-bearing San Angelo Formation of North-Central Texas is not an equivalent of the Chickasha Formation of central Oklahoma. This was never suggested in the initial geological or paleontological studies but has been misrepresented often in interpretive reports over the past 20 years. The San Angelo occupies a lower stratigraphic position in the Pease River Group of Texas than does the Chickasha in the coeval El Reno Group of Oklahoma. 2. There are no unambiguous lithostratigraphic data to support an equivalency between the widely separated San Angelo Formation of Texas and the Glorieta Sandstone of New Mexico. Historically, this correlation was based upon marine fossils and the position of these units on opposite sides of the Permian Basin.
Acknowledgements  A. A. Klymiuk  of the  Field Museum of Natural History and Sam Brown of the University of Chicago Library  clarified paleobotanical and archival matters related to E. C. Olson. We are indebted to  C. L. Hotton  for her palynological insights and grateful to G. P. Wahlman and  M. K. Nestell  for information on microfossils. We thank  G. Piñeiro  for drawing our attention to the putative last sphenacodontid from the  Yaguarí Formation. Special thanks to both  Diane Scott , who photographed the Macroleter agilis holotype, and to  R. R. Reisz , who shared the image for inclusion in this paper. This paper has been improved by comments from  Neil Brocklehurst. We thank the editorial staff of the  BSGF  for their efficient handling of the draft.
Ten µL of each DNA sample (200 ng) was digested with the ApekI enzyme (New England Biolabs, Hitchin, United Kingdom). Digestion took place at 75 • C for 2 h and then at 65 • C for 20 min for enzyme inactivation. The ligation reaction was completed in the same plate as the digestion, using the T4 DNA ligase enzyme (New England Biolabs, Hitchin, United Kingdom) at 22 • C for 1 h. Then, the ligase was inactivated prior to pooling the samples by holding it at 65 • C for 20 min. For each library, ligated samples were pooled and PCRamplified in a single tube. Genome complexity was reduced using PCR primers with one selective base (A) as described by  Sonah et al. (2013). Pair-end sequencing was performed on one lane of an Illumina HiSeq4000 platform at Genewiz facilities. RAW sequencing data were cleaned with cutadapt  (Martin, 2011)  and demultiplexed with GBSX  (Herten et al.
, 2015). Clean demultiplexed sequencing data from GBS are available in the NCBI SRA (Sequence Read Archive), under accession number (Submission ID: PRJNA862483). SNP genotype calling was performed with the GATK4 pipeline (Van der Auwera and O'Connor, 2020). The reference genome was constituted by the nuclear scaffolds of the Clementine v1. 0 genome assembly 1  , the mitochondrial  (Yu et al. , 2018), and chloroplast (Bausher et al. , 2006)  reference genomes from C. × sinensis (available in NCBI with reference NC_037463 and NC_008334, respectively). Positions with less than 10 reads were considered as missing 10. 3389/fpls. 2022. 1009350 data. Polymorphic positions were filtered for diallelic SNPs and minor allele frequency greater than 0. 01.
Genetic data analysis Neighbor-joining analyses for nuclear and mitochondrial data as well as a factorial analysis for nuclear data were performed to describe the genetic diversity organization of the analyzed germplasm. Both analyses are based on a dissimilarity matrix established from SNP genotyping data using the Manhattan dissimilarity index: D ij = 1 K K 1 |x ik -x jk | where i and j are the two individuals, k is the locus, K is the total number of loci, and xik is the frequency of the alternative allele at locus k for the individual i. All analyses were performed with DARwin software version 6. 0 2. Ancestral genome contribution along the genome of the admixed genotype was analyzed based on the distribution of homozygosity and heterozygosity of ancestral diagnostic SNPs (DSNPs). Fourteen accessions were used to select diagnostic SNPs for 10 ancestral species: two accessions for C.
Genotyping analysis After filtering the diallelic SNPs for less than 20% of missing data with the VCF tool, we obtained a genotyping matrix of 49187 diallelic SNPs for 27 accessions for the nuclear genome and a matrix with 40 SNPs for the mitochondrial genome. Only five SNPs were obtained for the chloroplast genome and were not found useful for maternal inheritance analysis. The analysis of heterozygosity performed for the Fundecitrus and Corsican accessions in comparison with already published accessions (Figure  4A ) allowed to identify pure species representative with most of the heterozygosity distribution curve under 10% (Figure  4B ) from admixed and interspecific hybrid accessions (Figure  4C ). Among the Fundecitrus germplasm M. warburgiana, M. garrawayae, M. inodora, E. glauca, and C.
Acknowledgments We thank  André Luís Sanches ,  Miguel Antonio Mendes , and  Sidnei Ferreira Alkimim  for their excellent help with grafting for propagation, Las challenge inoculations, and plant maintenance;  Fernanda Benedito  for assistance with DNA extraction;  Juan Cifuentes-Arenas  for suggestions and critical comments;  Tamiris Garcia  and  Afranio da Silva Aquino  for photograph assistance; and  Embrapa Cassava and Fruits  for providing some genetic resources.
Funding This study was financed in part by  Fundecitrus , grant no. 817526  from the  European Union H2020   Innovation Action Program  and Project  PID2019-104569RB-I00  from the  AEI-Spain , the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES) -Finance Code 001. MA was recipient of a Ph. D. fellowship from  CAPES. JF (# 312089/2019-8 ) and  NW  (# 313812/2021-7 ) were recipients of a fellowship grant from  Conselho Nacional de Desenvolvimento Científico e Tecnológico -Brazil (CNPq).
Model Setup The TC wind and surge modules of probabilistic modelling platform CLIMADA (Aznar-Siguan and Bresch 2019; Bresch and Aznar-Siguan 2020) were used to simulate TCs in current and future scenarios in order to evaluate damage to assets. CLIMADA is used to estimate the expected current socio-economic damage as well as the incremental increase due to economic and population growth, as well as climate change over the next decades.
Hazard TCs are among the most harmful natural disasters worldwide causing on average $29 billion in losses and affecting 22 million people each year (Guha-Sapir 2017). TCs are very prevalent in the Western Pacific basin and hit Viet Nam at least annually. A TC hazard dataset for wind and surge was created for 1980-2020. The wind hazard dataset is based on the global International Best Track Archive for Climate Stewardship (IBTrACS)  (Knapp et al. 2010 ) which includes 269-recorded events making landfall in the domain. Figure  1  represents the mean wind intensity and storm surge within the Western Pacific basin for 100-year return period TC events making landfall in Viet Nam. Although the historical event set is 40 years, with resampling within CLIMADA, we can access the 100-year return period (Aznar-Siguan and Bresch 2019).
Holland 2008 elaborates the internal methodologies of CLIMADA for wind field and the surge hazard dataset (flood depth) is derived from wind intensity with a linear relationship that modifies the water level according to the local elevation and distance to the coastal line  (Xu 2010). The hazard dataset is created on an irregular grid with: 30 arcsecond (~1km 2 ) resolution along the coastline and going 50km inland as well as over major population centres, which cover more than 95 percentile of the population; and a coarser resolution of 150 arcsecond for the rest of the country. To account for uncertainties and model sensitivity, each historic TC is resampled 100 times creating a large sample of stochastic events (a representative set of simulated events and process is detailed in Aznar-Siguan and Bresch 2019).
For simulating surge, Climate Central's proprietary, high-accuracy Digital Elevation Model (DEM) known as CoastalDEM  (Kulp and Strauss 2018)    The future hazard dataset is created with two future Representative Concentration Pathways (RCPs), RCP4. 5 and RCP8. 5, selected to represent the medium scenario and the worse-case scenario, respectively, in terms of climate change. While the current climate risk is simulated through CLIMADA's TC module using IBTrACS from 1980 to 2020, the future climate scenarios are also simulated through CLIMADA's TC module but based on assumptions from  Knutson et al. 2015 , which stipulate increased intensity and reduced frequency of TCs in the Eastern Pacific basin for both RCP4. 5 and RCP8. 5 scenarios by 2050. In order to perform a sensitivity analysis of the model, an alternative set of assumptions is used to simulate the future climate risk: increased intensity  (Knutson et al.
Exposure Physical risk is modelled as the direct economic impact of disasters and ideally represents the spatial distribution of physical asset stock. Aggregate estimates of asset values available through open-source data at national or sub-national level are used in this study for spatial distribution of asset values across the country and are re-distributed through CLI-MADA using the LitPop distribution. Estimates of total asset values are derived from gross domestic product (GDP) given their strong correlation  (Kuhn and Ríos-Rull 2016). Human presence and activity measured through linearly scaled night light intensity from satellite imagery is used for downscaling GDP based on geographical population data as detailed in  Eberenz et al. 2020. This process is also known as LitPop distribution.
This dataset includes a statistical assessment of exposure of residential houses given fixed socioeconomic conditions. The baseline statistics from GAR-15 give an estimate of $111. 3 billion in total housing exposure in Viet Nam in 2015 which increases to $125. 9 billion in 2020 applying the 2. 5% annual economic growth assumption (Table  1A ). This was downscaled to 30 arcsecond (approximately 1 km resolution) with LitPop. For future exposure, housing stock is assumed to double by 2050 to $251. 8 billion (in line with 2. 5% annual growth). This estimate was then downscaled with LitPop distribution. Figure  2  represents the current and future distribution of housing value exposure. To assess the current and future agriculture exposure in Viet Nam, the GFSAD30SEACE cropland extent dataset was converted to agriculture production using these values as shown in Fig.
There are however significantly more people affected by surge than by wind. Currently 1. 94 million people are estimated to be impacted by storm surge along the coastline and in low-lying areas, increasing to 2. 08 and 2. 16 million people under RCP4. 5 and RCP8. 5, respectively, assuming no change in TC frequency. In the case of decreased TC frequency, the number of affected people decreases to 1. 6 and 1. 41 million for RCP 4. 5 and RCP8. 5, respectively. The damage to agriculture and housing along with number of affected people is considerably higher for surge (with most damage limited to coastal areas) than damage from wind (that affects inland areas as well) but due to lack of any observational dataset we can't compare these to any events in historical period. However this can be attributed and explained by the fact that the asset and people exposure is higher in coastal regions, Fig.
Future Outlook Observational economic and displacement data due to TC events in the region is scarce, leading to a significant limitation in the current study with respect to model calibration for historical TC events. While EM-DAT and DesInventar do provide some observational data for historic events, the estimates are not comprehensive for the three selected asset classes. The authors refrained from modifying these observations on loss and people affected in order to compare them to CLIMADA output, as any assumptions used in the modifications would introduce uncertainty in the observational dataset. Thus there is increasing need of research in the direction wherein we could use event based observation to calibrate and compare the historical loss and damages to various asset categories from various natural hazards.
It would have been preferable to use a multi-hazard model representing TC hazard as a combination of wind, surge and rain-induced flood and landslide events as concluded from  Eberenz et al. 2020 however, CLIMADA  does not yet have modelling capabilities for raininduced flood and landslide. To note that the existing surge module used in the study did incorporate some advancements. Refined surge depths and in turn better impact assessment of surge damage was possible thanks to the improved CoastalDEM dataset used instead of SRTM3 data. Both datasets were analysed and it was found that the resolution was better resolved and represented in CoastalDEM for impact analysis of TCs. Finally, the cost and benefits of the adaptation measures currently are taken from various published and unpublished literature, along with communications from experts in the region, and would vary from year to year and region to region.
A risk transfer solution through insurance was presented that would allow this residual risk to be halved on average. However, adaptation measures were seen as critical in order to keep insurance costs at feasible levels. In terms of future research, observational data limitations of the current study demand further analysis in order to have a reasonable comparison to the model output. There is equally a need for multi-hazard analysis using CLIMADA. Improvements in DEM are notable and have helped in improving the analysis of surge in this data-scarce region.
Acknowledgement We would like to acknowledge various data providers for producing and making available their observational/model data. This study was kindly funded by the  KfW German Development Bank on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ). Funding Open Access funding enabled and organized by Projekt DEAL. This study was kindly funded by the  KfW German Development Bank on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ).
Collection operations Collection operations encourage visitors to provide different types of items that will enrich the exhibition. An example of this is the McCord Museum in Montreal, which calls on visitors to describe the images in their collection by tagging pictures on their online documentary library, thereby providing non-expert metadata that helps the museum understand visitors' relationship to artwork. The purpose of these collection operations is threefold: fostering a privileged relationship between the public and the museum by showcasing visitor contribution; documenting collections; involving visitors so that they feel concerned by questions of heritage. By showcasing visitor production, the museum hopes that notions of collection and heritage will become more meaningful to them.
These projects are not solely initiated by cultural institutions. They can indeed also be designed by other structures, as with the "Wiki Loves Monuments" project. This contest aimed to enrich the photographic database of heritage sites by inviting Internet users to upload pictures to Wikimedia Commons. This type of project is part of a reflection on the sharing of common goods. Lastly, crowdfunding has been used by museums as a way to appeal to visitors to raise funds in order to acquire or renovate items in museum collections. At the MUDO-Musée de l'Oise, crowdfunding helped restore Thomas Couture's painting L'Enrôlement des volontaires. In exchange for their participation, visitors can take part in events that strengthen their bond with the museum. Donators might be invited to the opening of a show, get behind-the-scenes access to the museum or engage with conservators and restorers.
Tracking devices were programmed to record fixes (i. e. , GPS positions) at 5-10-min intervals from 1 h before sunrise to 1 h after sunset (see Table  1  for details of the tracking devices and sampling frequency). GPS data were incorporated into the Movebank online data repository (www. moveb ank. org). Data were standardized by resampling the GPS fixes to 15 min for each individual to homogenize our dataset. Vultures were tracked on average 1040 ± 809 days with a mean number of fixes per individual of 41,335 ± 40,493.
| Data analysis We analyzed the effect of sex, season, and breeding regions on the monthly home-range sizes, cumulative monthly distances traveled (normal error distribution, identity link function), and percentage TA B L E 1 Annual and monthly homerange size (in km 2 ; estimated at 95% KDE), and cumulative distance traveled (in km) of 127 adult Griffon vultures tagged with GPS in Spain. of monthly overlap (beta error distribution, identity link function) using generalized linear mixed models (GLMMs; glmmTMB package,  Brooks et al. , 2017). Sex, season, and breeding region were included as fixed factors, while individual identity and year were included as random terms in the models to avoid pseudo replication. We did not divide the period into breeding stages since we could not rule that all individuals were breeding each monitoring year and, therefore, followed the same breeding cycle periods.
g. , sex differences in movement associated with particular regional conditions) and (  2 ) to avoid speculations without any empirical foundations in the interpretation of the results. Models were compared using the Akaike Information Criterion corrected for small sample sizes (AICc;  Burnham & Anderson, 2002). The best model was the one with the lowest AICc value. All models with a difference of ΔAICc < 2 were considered alternatives  (Burnham & Anderson, 2002). For the best model, homogeneity of variance and normality of residuals was inspected by using the "ggresid" package to check the goodness-of-fit of our best models  (Goode & Rey, 2019). We estimated the variance explained by the fixed and random factors by using the "performance" package  (Lüdecke et al. , 2021) , while differences between their levels were assessed through Tukey's post hoc tests using the "emmeans" package  (Lenth, 2022).
, 2022; Pinet et al. , 2012). Our results showed that females have larger home-ranges and travel farther than males. These findings align with other studies in which the same dataset was analyzed and where females exhibited larger traveled distances than males during reproduction (see  Delgado-González et al. , 2022; Gangoso et al. , 2021) , including other vulture species (see  Bamford et al. , 2007; García-Jiménez et al. , 2018; Kane et al. , 2015; Krüger et al. , 2014; Margalida et al. , 2016). Contrary to our expectations, we also found sex differences in the monthly home-ranges fidelity. Males showed greater fidelity than females, indicating that the latter use different areas throughout the year. This gender variation in seasonal fidelity may respond to differences in resource selectivity  (Delgado-González et al.
In Angola, active molecular surveillance for dengue, YFV, chikungunya and Zika viruses, began in January 2017 at the National Institute of Research in Health (INIS) in Luanda. INIS currently seeks to confirm dengue suspected cases from hospitals and clinics across Angola using real-time reverse transcription PCR (RT-PCR), and in some cases through virus whole genome sequencing. Analysis of DENV genome sequences can help to disentangle the origins of viral lineages and their temporal dynamics. Despite several logistic challenges regarding the generation of viral genome sequence data during outbreaks, particularly in low-and middle-income countries  [29] , in January 2018 we conducted the first on-site genomic sequencing of a single dengue virus strain in Angola  [30]. Here we analysed clinical, demographic and epidemiological data from 351 suspected dengue cases sampled across 13 provinces in Angola, including cases of severe dengue in Angola.