dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the data we work with have 8 þ 18 ¼ 26 dimensions for XPA datasets. Therefore, dimensionality reduction is required before one can use the KDA approach. The final KDA-based classifiers are depicted in Fig. 3. Technical details of constructing such classifiers are beyond the scope of this paper. For additional information, readers may consult, e. g. ,  Gramacki and Gramacki (2017) ;  Gramacki (2018) ; Cha� con and Duong (  2018 ) where simple demonstrative examples are presented and a basic mathematical background is given. Kernel Density Estimate (KDE) contours (one contour for data belonging to every given region XP27, XP61 and XP90) are marked with different colors. White color represents areas where the probability density is practically zero. For points located in this area it is not possible to assign them to a specific class.
3 , graphs on the left). The same was also the case for the AD dataset (see Fig. 5 ). A question may be asked whether the KDA method has any advantage over classical parametric methods (see Section 3. 2). While comparing the results in Tables  4  and 5 , it can be seen that the LDA and KDA-LDA methods produce the best results and these results are identical for data that is analysed. There is no question that an advantage of the KDA method is an opportunity for a rapid visual assessment of the analysed data. In particular, one can quickly assess how data from individual classes are arranged relative to each other. On the other hand, one major disadvantage of the KDA method is its relatively slow computation time. Parametric methods usually do not offer such a possibility, especially if the data is strongly multidimensional.
One must of course bear in mind that while using the KDA method a certain loss of information is inevitable. It results from the need of reduction of multidimensional data into 2D or possibly 3D. In the case of the XPA-SLAG data this loss is anyway not very significant, especially for the LDA method, where the first LD explains 83% of the between-group variance in the data. A PCA-based dimensionality reduction method should theoretically produce somewhat worse results than the LDA-based reduction. However, in this paper results produced by both methods are given. It seems therefore that the use of the KDA method is always worth considering, even if it does not offer a decisive advantage over more classical approaches. A sort of shortcoming of the KDA method is its computational complexity.
Final results of SI provenance for the XPA dataset The final results are summarised in Table  S1  in Supplementar-y_Materials. html file in Electronic Supplements. Numbers in parentheses indicate a posteriori probabilities of a choice of a given class (XP27, XP61, XP90). The XP column is the number of experiment in which a Fig. 2. Visualization of the XPA-SLAG and the XPA-SI datasets' transformations according to the LDA and PCA dimensionality reduction methods. Next, the XPA-SI testing set was projected into the space created by the XPA-SLAG training set (plots in right column). Such 2D projections show that they are much more dispersed (as should generally be expected). In this example the first LD explains 83% of the between-group variance in the data while the first PC explains only 53% of the total variability in the data.
Experiments with the XPB datasets (XP17, XP23, XP26) As mentioned above, due to considerable differences between the ore chemistries that were used in XP26, XP61 and XP90, it was decided to test the proposed approach with the use of data from another three experiments. For detailed results, the reader is sent to the Supple-mentary_Materials_XPB. html file in the Electronic Supplement and in this section only the main points are discussed. The LDA demonstrated a reasonable separation between both the test set and the training set (XP17; XP23; XP26), although some overlapping between XP23 and XP26 can be seen. The result is worse for the PCA, due to reasons stated in Section 3. 3. Concerning the validation of the classification, the best results were produced by the RF and the SVM methods, while the performance of the KN approach seems to be the worst.
As regards the confusion matrices of the SI data, it was the NB and the SVM methods that performed the best, while the results yielded by the LDA were the worst. As regard the final results of the provenance assessment, all SI observations from the XP17 dataset and most SI observations from the XP26 dataset were correctly classified, while there was some confusion concerning the XP23 dataset. All in all, the obtained results demonstrate that the performance of the proposed method is sound and reasonable.
Experiments with the AD datasets Our main goal is to build a classifier using the AD-SLAG data and then to classify the AD-SI data using this classifier. We use exactly the same methodology as it was discussed in Section 3. Therefore, in this section, we do not repeat the information given therein and only discuss the final results. Table  6  offers results of assessing the quality of classifiers. The results are generally very good for parametric methods; however, the results for the two variants of the KDA method are slightly worse. In the case of the AD assemblage we have as many as 7 classes, while there were only 3 classes in the XP data. In such circumstances, the KDA approach becomes slightly less efficient. What is more, the numerical strength of individual classes is sometimes not very high, e.
g. , for 'SaH' there are only 6 observations (see Table  1 ). This is perhaps the main reason why the results produced by the Holdout, K-kold CV oraz Leave-one-out CV methods are a little worse (but these are still not bad). As mentioned before, in order to use the nonparametric KDA classifier the nD→2D dimensionality reduction is required. In order to choose the best 2D conversion method, an experiment which is similar to the aforementioned one was carried out, (see Fig. 4 ). As it was the case with the XP data, the LDA-based dimensionality reduction method produces better results and individual classes are better separated. This visual assessment is also confirmed by the numerical results. In this example the first LD explains 65% of the betweengroup variance in the data while the first PC explains only 53% of the total variability in the data.
It is also worth stressing that data from 'Bar' and 'SaM' classes, as well as 'Baj', 'Fer', and 'Min' classes are practically non-separable. Thus, it cannot be expected that the KDA method will perform well. As regards the AD-SI data, after the reduction to 2D it can be seen that both for the LDA and PCA methods individual observations are allocated to one pretty compact region. On this basis it could be tentatively expected that they come from one region or several regions which are similar to one another. Final results of SI provenance classification for the AD dataset are given in Table  S2  in Supplementary_Materials. html file in Electronic Supplements. In addition, the classification results for two variants of the KDA method are also included in this table. The KDA-LDA and KDA-PCA columns present results for the case where 2D reduction was done using the LDA and PCA methods respectively (see Section 2.
4). The two methods give in fact different results and unfortunately it is not possible to reliably indicate which one should be used. The graphical presentations of the KDA-based classifiers are depicted in Fig. 5. It can be seen that both classifiers give essentially different results. In principle, it is not possible to indicate which results are better and which ones are worse. The final results are to a great degree convergent with those achieved by Disser and his team  (Disser et al. , 2017). After the application of the 'classifier voting technique' (see Section 2. 6) a vast majority of the AD-SI data is classified into the 'Min' class. Concerning these observations which were assigned to different classes, it can be assumed that the final classification produced by a set of independent methods can perhaps be considered more reliable.
The experimental data assemblages were used to test our new approach to the issue of classification. On the basis of these assemblages we demonstrate technical details of the method (Section 2) and then we prove its considerable practical value (Section 3). A significant new element is the use of the so-called Classifier Voting Technique. Thanks to it, we are able to strongly enhance the credibility of final results of classifying of archaeological artefacts of unknown provenance to individual regions. What is more, we can express this trustworthiness in a quantitative manner as a probability of classification of a given observation to a given group. The experimental assemblages also underwent classification with the use of the non-parametric method, that is, Kernel Discriminant Analysis (KDA).
The classification results for this assemblage are offered in Section 4. On the basis of the AD assemblage 6 different parametric classifiers (using the aforementioned LDA, SVM, NB, RF, KNN, and RPART methods) and 2 nonparametric KDA classifiers were built. In the latter case, two different methods of data multidimensionality reduction were applied, that is, Linear Discriminat Analysis (LDA) and Principal Component Analysis (PCA). The quality of these classifiers was verified with a series of methods which are typical for such cases (see Table  6 ). Then, the final (i. e. , test set) data was classified. As in the case of the experimental assemblages, the final classification of the AD test data (SI group) was obtained with the use of the classifier voting technique. The final result is in most cases convergent with that obtained by  Disser et al.
(2017). As said above, the classification of such observations that were assigned to different classes than in  Disser et al. (2017)  is perhaps more credible, as it has been produced by the voting technique on the basis of a few independent methods. It is worth stressing that the results of classification of the AD assemblage with the use of the KDA method are pretty debatable and thus a practical value of this approach is rather dubious. Although very good results were obtained for the experimental assemblages, the classification obtained for the AD data demonstrates that this is not necessary a general rule. What is a significant problem here is a great discrepancy of results (see Fig. 5 ). This is caused by the fact that a key operation in the KDA method is multidimensionality reduction of input data.
Results of the first online survey The questionnaire was sent to 600 harbor and mooring area managers and 400 managers of private boatyards. Managers for whom no email address could be identified could not be contacted. A total of 321 complete responses were recorded: -33. 6 % of the requested port or anchorage area managers replied; -13. 6 % of the managers of nautical shipyards contacted replied. It should be noted that several managers have sometimes been identified for the same structure. Several responses were therefore sometimes received for the same site. The actual response rates to the survey are therefore as follows: -Ports and anchorage areas: 26 % -Nautical sites: 8 % The types of managers who responded to the survey are illustrated in the figure  1. Given the difficulties encountered in identifying managers of private boatyards and their low response rate, data concerning them is scarce.
Discussion In addition to the updates that will be carried out annually by the Water Police services, the database could be supplemented by the following actions: -Additional information could be collected via telephone surveys supplemented if necessary by field visits, in particular for large capacity ports; -The GIS could be improved by the geolocation of all the mooring areas; -The location of nautical sites could also be improved. Data from the Port Sediment Quality Monitoring Network, the REPOM, could be analyzed in order to identify possible contamination of the receiving environment by contaminants that may be present in the fairing discharges (TBT, Cu, Zn, etc. ).
Methods The method proposed was a comparison between Brazil and France about stroke care by collecting secondary data (already existing in the national databases or on request). The health policies concerning stroke care developed in both countries (care and prevention) and the health indicators related to care to better management were studied to achieve the objective. The populations under analysis comprise the entire population of metropolitan France and Brazil. The period analyzed was 2010 to 2017. The data for the National healthcare policies from Brazil and France were searched in the Ministry of Health websites (National Health legislation -Circulaire DGOS/R4/ R3/PF3 n°2012-106; Loi n°2004-806 du 9 août 2004 (France)); Portarias nº664 and 665/2012 (Brazil). From a careful reading, the main points related to stroke care were described in Table  1.
Data about stroke's prevalence by age group (total cases in a year) were collected from the Hospitalization System of the Unified Health System, obtained in monthly files, according to the principal diagnosis of the discharge note. These files were consolidated in annual periods. For France, data were obtained by request to the Programme de Médicalisation des Systèmes d'Information PMSI. The data for the acute hospitalizations and the hospital mortality rate per 1000 inhabitants, the following formulas were applied: For healthcare prevention, the data about risk factors indicators were collected from the WHO website. The data referring to the estimated population were collected from the United Nations website. The lack of adjustment of differences in the case-mix and the absence of comparable indicators on post-stroke functional disability can be pointed out.
We performed an empirical comparison of the treatment hierarchies obtained with PreTA, pBV and SUCRA, calculated using parametric bootstrap in a frequentist framework. The agreement between ranking metrics was measured using Kendall's tau. We used a previously described database of NMAs published until 2015 including networks of four or more interventions  (1). We included networks with available outcome data in arm-level format, for which the primary outcome was analysed either as binary or as continuous. We used the effect measure used in the original review. Details about the inclusion criteria of the NMAs included in the database can be found in  (1). The empirical analysis was performed with the use of the nmadb package in R (20). In the following section, we illustrate our method in two networks of interventions, for which at least some disagreements between pBV, SUCRAs and PreTAs occur.
Methods The systematic review was performed in accordance with PRISMA guidelines  [10]. This review involved a systematic search of the electronic databases MEDLINE/PUBMED, Google Scholar, the Chinese Si-noMed (http://www. sinomed. ac. cn/zh/), Ovid Medline, Scielo, Embase, Scopus, and the Database of Abstracts of Reviews of Effects. In addition, Otolaryngology-Head and Neck Surgery (OHNS) societies and institutions across the world were contacted (Table  1 ). Guidelines and recommendations from January 2020 to April 2020 were included. No language restriction was applied to the search strategy. Search terms (medical subject headings or keywords) included: "tracheotomy", "tracheostomy," "SARS CoV-2", "COVID-19", "guidelines/practice guidelines/clinical guidelines," "consensus," "ENT," and "Head & Neck surgery.
X-ray diffraction A single crystal of BTBT was selected, mounted onto a cryoloop, and transferred under a cold nitrogen gas stream. Intensity data were collected with a BRUKER Kappa-APEXII diffractometer with micro-focused Cu-Kα radiation at 200 °K. APEX 3 suite and SAINT program (BRUKER) were used to carry out data collection, unit-cell parameters refinement, integration and data reduction. SADABS (BRUKER) was used for scaling and multi-scan absorption corrections. Despite large diffraction peaks in some direction due to the shape of crystal, in the Olex2 suite  [19] , the structure were solved with SHELXT-14  [20]  program and refined by full-matrix least-squares methods using SHELXL-14  [21]. CCDC 1956856 contain the supplementary crystallographic data for this paper. The data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www.
Overview To investigate how gliding has affected body size evolution, we combined species data on body size and lifestyle (whether they were gliders, arboreal, or terrestrial) from the literature and our own observations in the field. These data were then coupled with new phylogenies developed from molecular data obtained from GenBank in order to apply statistical methods designed to reveal the potential dynamics of phenotypic evolution. We focused on body size in our study of morphological differentiation for two reasons. First, body size (and mass in particular) is an important determinant of gliding performance  (Losos et al. 1989; McGuire 2003; McGuire and Dudley 2005). Any conflict in selection pressures associated with ecological diversification and competition versus those maintaining gliding performance should be especially clear in the dynamics of body size evolution.
Data Body Size, Lifestyle, and Gliding Performance. We used two data sets of body size for the dragon lizards (both originally compiled from a variety of sources by  Feldman et al. [2016] , including original data on Draco reported in  Mc-Guire [1998] ; we also supplemented these data with an additional species from  Kalahroudi and Hojati [2015] ). The first data set included maximum snout-to-vent length (SVL; n p 458 species; table  S1 ; tables S1-S14 are available online), while the second covered a more limited set of species but included data on mean SVL (mm) and mean mass (g; n p 110 species; table  S2 ). For the squirrels, we compiled data on species means for length (n p 220 species) and mass (g; n p 210 species) from a variety of sources (table  S3 ). Information on lifestyle (arboreal, terrestrial, and gliding) for both families (n p 295 dragon species, n p 183 squirrel species) was obtained from a variety of sources (tables S4, S5).
Phylogeny We developed new phylogenies for the dragon lizards and squirrels using molecular data retrieved from GenBank  (Benson et al. 2012; tables S7, S8). Methodological details are provided in full in the supplemental material. A concise summary is given below. For the dragons, we used five genes that previous studies have shown provide the greatest coverage of species  (Pyron et al. 2013). Our final alignment of these genes included 297 species (including two outgroups) and a total of 4,637 bp distributed as follows: 12S rRNA (12S, 1,103 bp), 16S rRNA (16S, 582 bp), NADH dehydrogenase subunit 2 (ND2, 951 bp), brain-derived neurotrophic factor (BDNF, 690 bp), and RAG1 (1,311 bp). For the squirrels, we used six genes frequently used by previous phylogenetic studies of this family  (Mercer and Roth 2003; Fabre et al.
2012)  and included 220 species and a total of 6,001 bp distributed as follows: 12S rRNA (12S, 378 bp), 16S rRNA (16S, 367 bp), cytochrome oxydase 3 (COX3, 783 bp), cytochrome b (CYTB, 1,140 bp), interphotoreceptor retinoid-binding protein (IRBP, 1,188 bp), and recombination-activating protein 1 (RAG1, 2,145 bp). Phylogenetic analyses were conducted using the package BEAST 1. 8. 0  (Drummond and Rambaud 2007)  under a lognormal relaxed molecular clock  (Drummond and Rambaud 2007)  and two fossil calibration points per family. For both families, three independent Markov chain Monte Carlo (MCMC) chains were run for 50,000,000 generations each and sampled every 5,000. Tracer version 1. 6  (Rambaut and Drummond 2007 ) was used to confirm convergence, good mixing, and stationarity of each MCMC chain.
The best-supported model (DAIC c p 0:0) was also used to compute residuals of mass to consider mass-specific evolutionary change free of the influence of body length. Given our a priori expectation that body size has evolved under strong selection imposed by lifestyle (especially gliding), we applied regressions using models that assumed an Ornstein-Uhlenbeck (OU) process implemented in the slouch package version 1. 2  (Hansen et al. 2008). This method uses maximum likelihood to estimate the rate of adaptation pulling the phenotype toward an optimum and deviations from the optimum caused by unconsidered factors. These estimates are presented as phylogenetic half-life (t 1/2 ), which is the length of time in millions of years that size has taken to evolve halfway from its ancestral state to its inferred adaptive optimum and the magnitude of the stationary variance (v y ) of species around the optimum.
Tests of Character Displacement in Gliding Dragons. To test the hypothesis that competition among sympatric Draco species has promoted body size divergence  (McGuire 2003) , we calculated differences in body length and residual mass among co-occurring congeners and compared these values with null distributions of differences generated by simulating evolution without regard to species co-occurrence. We obtained 1,000 simulated data sets performed along the Draco phylogeny under both BM and OU processes in which parameters were allowed to vary across the mid-95% range obtained from OUwie-estimated values. All simulations were performed using phytools  (Revell 2012). For each simulated data set, we estimated the mean Euclidean distance of body size among sympatric species and the mean Euclidean distance of body size among allopatric species.
Acknowledgments This study was partly supported by an  Endeavour Research Fellowship  to J. G. P. awarded by the  Department of Education and Training, Australian Government  (ID:  5471_ 2016 ). We thank  Isaac Casanovas  for his advice about squirrel fossils. Pioneering studies by  Dick Thorington ,  Larry Heaney , and  Jim McGuire  provided key foundations for this work and inspired our own investigation into the potentially competing evolutionary challenges faced by gliding animals.
(2017)  discussed the meridional spread of the Calbuco aerosols toward the South Pole, which was modulated by the Quasi-Biennial Oscillation. The present paper reports on the sun photometry observations of the Calbuco plume at six South American and three African sites. The geographical location of these sites is helpful for improving the discussion on the latitudinal distribution of the Calbuco plume. Following the Puyehue-Cordón Caulle eruption, an effort was made to deploy the CIMEL sun photometer system over Argentina in order to detect aerosols from volcanic ash and Patagonian dust  (Otero et al. , 2015). These new databases, which have been integrated into the Aerosol RObotic NETwork (AERONET) global network since 2012 and 2013, will be used and analysed in this study. The usefulness of sun photometry measurements in the investigation of aerosols from major and moderate eruptions has been reported in many previous works (e.
The estimated uncertainty in the AOD measurements under cloud-free conditions ranges from 0. 01 to 0. 02  (Dubovik et al. , 2000 (Dubovik et al. , , 2006;; Eck et al. , 2003 Eck et al. , , 2005)). A detailed description of the CIMEL sun photometer, the AERONET network, and the associated data retrieval is given by Holben et al. (1998). The AOD values presented in this work are selected at Level 2. 0 (cloudscreened and quality assured) and were downloaded from http://aeronet. gsfc. nasa. gov/ (last access: 5 March 2020). All of the available observations performed before the Calbuco eruption until 2016 are also used for this work. The available daily observations and the associated period for each site are reported in Table  1. The number of available daily observations range from 242 to 3495 (Durban and Neuquén respectively) (Table  1 ).