dataesr/scientific-paragraphs-categorization huggingface dataset:
esac. esa. int/Docs/PACS/pdf/pacs_om. pdf scales). A study of the CH3CN chemistry using the combined A+B+D data can be found in  Gieser et al. (2019). The study of the kinematics of the disc (if any) from the combined data set will be the focus of future publications (e. g. Ahmadi et al. in prep. ). The beam size of the compact configuration continuum observations is 2. 3 × 1. 6 PA = 88. • 7, whilst the beam size of the combined extended configurations is 0. 48 × 0. 38 PA = 56 •. Vector-averaged radial visibility profiles were extracted, where we used the standard error of the mean of the points in each uv-distance bin as measure of the errors. The profiles are shown in Fig. 2  whilst the CLEAN images are shown in Fig. 3. The NOEMA/CORE A+B observations recover more emission towards smaller baselines than previous PdBI 1.
The local standard of rest (LSR) velocity measured by  Kaźmierczak-Barthel et al. (2014, -5. 5 ± 0. 5 km s -1 ) over 24 lines in Herschel /HIFI observations was used to shift the velocities, so zero velocity corresponds to the line systemic velocity. We identified 3 continuum sources from the compact configuration data named NOEMA 1-3 and their positions and peak intensities are summarised in Table  2. The position of NOEMA 1 is consistent with the VLA 3 source, for consistency to previous works we will use the latter name. Fig. 3 (a) reveals the presence of at least 2 sources south of VLA 3. Thus, the NOEMA images (continuum and zeroth moment compact configuration maps) were fitted with three 2-D Gaussian components and the results are listed in Table  3. The observed size of VLA 3 decreases slightly with increasing K transition, i.
3 (b) shows a similar orientation. 5. 9 × 6. 6 a 56 ± 1 6. 1 22. 9 5. 9 20. 7 Notes. An error of 0. 02 arcsec is estimated for the semi-axes of the 2-D Gaussian fit. FW1% stands for full width at 1 per cent the peak intensity. a Note this value is from the PSF binned to the observed pixel size and rotated to match the observations, hence it is different from the one presented in Table  5. 2013 ) superimposed to the NOEMA compact configuration CH 3 CN J = 12 -11 K = 3 zeroth moment map (colour scale). Sources are labelled and the position of the continuum source NOEMA 3 is marked with a white star. Following  Johnston et al. (2013), contours are 3, 4, 5, 7, 10, 15, 20, 30-100×σrms  (σrms = 30 µJy beam -1 ). V a Type of data product used to compare observations and models: I for images, V for visibility profiles and P for azimuthally averaged radial profiles.
1-D LTE and non-LTE CH3CN modelling We fitted the methyl cyanide 12-11 K-ladder with the program cassis 8  to study some physical properties of the gas (e. g. temperature). This program uses a Markov chain Monte Carlo (MCMC) which samples the parameter space in order to minimise the χ 2 between the observed and synthetic spectra. We used one isothermal density component model to fit the observations and their parameter ranges are listed in Table  7. cassis calculates spectra under the LTE approximation, or the non-LTE approximation by using the radiative transfer code radex (van der  Tak et al. 2007). Two density component models were experimented upon, but similar results were obtained. In order to limit the region to analyse, only spectra with at least one line with a flux higher than 0.
Dust size distribution and grain growth In order to explore the change in dust from within the envelope or disc, we derived spectral indices based on dust emission at different scales from single-dish and interferometric observations in the radio/submm regime. The former traces cold dust located in the large-scale envelope, whilst the latter trace scales of a few 1000 au. The dust optical depth is assumed to follow a power law τ ∝ ν β in this regime (optically thin). If it is further assumed that the emission is in the Rayleigh-Jeans regime, the fluxes are then Fν ∝ ν β+2 ∝ ν α. Fig. 19  shows the observations made at different scales and the indices α of the power law fitted to the single dish, mm and radio interferometry data individually. Dust models with shallower emissivity indices (e.
A1. 2 Fitting procedure Table  5  lists the data product type (image, intensity radial profile, visibility profile) that we used to compare the observations and the respective synthetic observation in order to extract spatial information. To obtain each of these data products from the code results and compare them with the observed ones, we binned the images to match the observed pixel size, rotated them to match the cavity position angle (259 • ;  Preibisch et al. 2003 ) and convolved them with the respective PSF. The details about the processing of the model images can be found in Appendix C. In order to determine a set of models that best fit our data, we first calculated the reduced χ 2 , for each of the datasets listed in Table  5  and the SED. The reduced χ 2 were calculated after masking the non-relevant data (see Appendix C).
A3 CH3CN molecular line modelling Models for the K = 2 -5 lines were calculated using the line radiative transfer code mollie  (Keto & Rybicki 2010). We excluded the blended K = 0 and 1 transitions (see Fig. A1 ) to ease the modelling/analysis and because these should not add any additional information. The code requires grids describing the density, temperature and velocity distributions of the gas, the abundance of the molecule with respect to the gas density and the width of the line. mollie calculates the molecular line emission at different velocities, i. e. it produces a data cube, under the LTE approximation for CH3CN. Since the radiation field (the gas temperature) is calculated from the continuum modelling and the LTE approximation is used, mollie uses the input distributions to calculate the level populations and produce the images.
The 2-D Gaussian components were fitted simultaneously to the continuum images, whilst for the methyl cyanide zeroth moment maps each source was fitted separately within a box isolating each source. a From Leiden Atomic and Molecular Database (LAMDA). b The units of peak fluxes are mJy beam -1 for the continuum observations and Jy beam -1 km s -1 for the molecular lines. c Brightness temperature at the line peak at the position of the zeroth moment peak. d The units of flux are mJy for the continuum observations and Jy km s -1for the molecular lines.
ACKNOWLEDGEMENTS The authors would like to thank  T. Preibisch ,  J. P. Simpson  and  K. -S. Wang  who kindly provided their data. F. O. acknowledges the support of  CONICYT Becas-Chile 72130407. A. A. , H. B. , J. C. M. , and C. G. and S. S. acknowledge support from the  European Research Council  under the European Community's  Horizon 2020 framework program  ( 2014-2020 ) via the  ERC Con-solidator Grant â ȂŸ From Cloud to Star Formation (CSF)'  (project number  648505 ). HB also acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG)  via the  Collaborative Research Center  ( SFB 881 ) â ȂŸ The Milky Way System' (subproject  B1 ). A. P. acknowledges financial support from  CONACyT  and  UNAM-PAPIIT IN113119  grant, México. DS acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : " Building a Habitable Earth " ( SE 1962/6-1 ).
R. K. acknowledges financial support via the  Emmy Noether Research Group on Accretion Flows and Feedback in Realistic Models of Massive Star Formation  funded by the  German Research Foundation (DFG)  under grant no. KU 2849/3-1  and  KU 2849/3-2. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013). Part of this work is based on archival data, software or online services provided by the  ASI Science Data Center (ASDC).
DATA AVAILABILITY The data underlying this article will be shared on reasonable request to the corresponding author. Parts of these data were derived from: WFCAM Science Archive (http://wsa. roe. ac. uk/), Herschel Science Archive (http://archives. esac. esa. int/hsa/whsa/), SCUBA Legacy Catalogues (http://www. cadc-ccda. hia-iha. nrc-cnrc. gc. ca/community/scubalegacy/). The K-band speckle interferometric data were provided by T. Preibisch by permission.
Both in-deep analyses and overviews on a large quantity of research papers can help decision makers or even citizens to be better educated on the state of the art. For people, it can help move aside fake news. It can also help new comers in the COVID-19 research field providing overviews of subtopics first (main publication venues, main authors,. Indeed the research in the domain is quite huge specifically if we consider other forms of COVIDs, Severe Acute Respiratory Syndrome, and Middle East Respiratory Syndrome. For example, the recently released collection named COVID-19 Open Research Dataset 1 consists of more than 40,000 articles. Some papers start to provide reviews  6, 11, 15  which are indeed very useful. This paper aims at providing a systematic methodology to mine such a large publication set while giving some specific focuses on topics of interest.
Huang et al. 15  reviewed 1,281 abstracts from which they identified 322 manuscripts relevant to 5 areas of interest for their study. The five topics they chose are as follows: antibody kinetics, correlates of protection, immunopathogenesis, antigenic diversity and crossreactivity, and population seroprevalence. Dousset et al. 8  presents a short analysis of a larger dataset which size is similar to the one that we are using in this study. However their study does not go in deep in the publication content but rather analyze the collaborations between researchers and countries. They do not analyze any specific topic. Different from the previous analysis, this paper combines (a) the analysis of a large data set of about 25,000 references and (b) highly assisted analysis. While the methodology could be apply to various COVID-19 subtopics, we focus here on two main topics: the origin of the virus and the use of other disease treatment.
Mining scientific publications Most of the work in automatic publication analysis is related to scientometrics 26 which as been defined as "quantitative study of science, communication in science, and science policy"  13. In this paper, publication mining is not strictly a question of scientometrics, rather the objective is to build knowledge from a large set of publications. Shibata et al. 31  uses citation network analysis in order to detect emerging research. Small et al. also use direct citation and co-citation analysis in order to identify emerging topics  32. Unfortunately, this information is not necessarily provided in digital database for large quantities of documents. Buscaldi et al. mine scholarly publications to build scientific knowledge graphs  5. As in our approach, the authors use existing natural language processing and mining tools in their approach; however, they focus on the tex-tual parts of the publications.
COVID-19 scientific publication set Recently, publishers have released the COVID-19 Open Research Dataset. It is available at https:// pages. semanticscholar. org/coronavirus-research. This data set consists of multiple files. Among them, the Metadata file (60Mb) is a CSV file corresponding to 44,270 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications. The fields of the structure of the records are as follows: title, doi, abstract, date of publication, authors, journal, as well as internal document ids (PMC ID, PUBMED ID, Microsoft Academic Paper ID, WHO ID) and information whether the full text is available or not. While the meta file is a rich source of information, other pieces of information that are missing in that data file can be very useful such as the affiliation of the authors for example.
For this reason we also considered a more complete set regarding the attributes that are provided. We chose to focus on the documents from PubMed only 2  , which is known to be realable source. It does not contains all the 44k scientific papers from the COVID-19 Open Research Dataset but about 24,000 papers. The query used to query the PubMed collection (https://www. ncbi. nlm. nih. gov/pubmed) was the same as the one used in the submentioned data set: "COVID-19" OR "covid19" OR "Coronavirus" OR "Corona virus" OR "2019-nCoV" OR "SARS-CoV" OR "MERS-CoV" OR âĂĲSevere Acute Respiratory SyndromeâĂİ OR âĂĲMiddle East Respiratory SyndromeâĂİ.
(2019)  published a checklist of the land snails, including several species of Sarika. A year later, the genus Sarika from Myanmar and Cambodia were taxonomically revised, and two new species from Myanmar and three new species from Cambodia were discovered based on genitalia characters  (Pholyotha et al. 2020a (Pholyotha et al. , 2020c;; Sutcharit et al. 2020 ). However, knowledge on the genus Sarika is still fragmentary because most of the nominal species are described from Thailand. Therefore, we examined the anatomy of specimens collected during intensive field surveys throughout Thailand, and found that the diversity of this genus remains underestimated and its complex relationships are a mystery. This present study is the first comprehensive taxonomic treatment of the land snail genus Sarika based on conchological and anatomical characters and data on the mitochondrial cytochrome c oxidase subunit I (COI) gene.
Shells and genitalia were imaged using a digital camera and a stereomicroscope with Cell'D Imaging Software. Radulae were extracted, soaked in 10% (w/v) sodium hydroxide, cleaned with distilled water, and then imaged by scanning electron microscopy (SEM; JEOL, JSM-6610 LV). In the material examined sections, shells refer to empty shells while specimens refer to specimens preserved in ethanol. The Thai terms "Tham" meaning cave, "Wat" meaning temple, and "Phu" and "Khao" for mountain or hill are used throughout for the locality names. Descriptions of all new species herein are attributed to Pholyotha & Panha.
Nucleotide sequences were deposited in GenBank under accession numbers: MT894062-MT894119 (see Table  1 ). The COI gene sequences were edited and aligned using ClustalW, as implemented in the MEGA7 software  (Kumar et al. 2016). Genetic distances between Sarika species and related taxa were calculated using Kimura's two-parameter model (K2P) as implemented in MEGA7  (Kumar et al. 2016 ). The alignments were tested for substitution saturation using DAMBE  (Xia 2013). As no saturation was detected in the sequences (Iss < Iss. c with p < 0. 01), all the codon positions were used in the subsequent analysis. The phylogenetic analyses were conducted using maximum likelihood (ML) and Bayesian inference (BI) and both analyses were performed on-line through the CIPRES Science Gateway  (Miller et al.
2010 ). The ML analyses were performed by using the program RAxML-HPC2 on XSEDE v. 8. 2. 12  (Stamatakis 2014)  with 1000 bootstrap replicates using GTRGAMMA as the model. Prior to the BI analysis, Kakusan4  (Tanabe 2011 ) identified the best-fit model as follows: the general time reversible model  (Tavaré 1986 ) with gamma distribution for the first and the second COI codon positions, and the HKY model  (Hasegawa et al. 1985)  for the third COI codon position. The BI analysis was performed by using the program MrBayes on XSEDE v. 3. 2. 7a (Ronquist et al. 2012)  with two simultaneous runs. The analysis was run for 10 million generations (default heating parameter), sampled every 500 generations, starting with a random tree and burn-in set to 50%. Convergence of the two runs was achieved if the average standard deviation of split frequencies were ≤ 0.
* Sarika inferospira sp. nov. has a tail filament with a series of branching spines along its length, except near the middle part which is spineless. Table  5. Taxa attributed to Macrochlamys and Sarika from mainland Southeast Asia, as classified by their maximum shell width and maximum whorl numbers. Data are taken from the original descriptions and additional references. Taxa in bold are anatomically examined and have confirmed generic placements. The superscript indicates references: 1 Godwin-Austen (1907) and  Blanford and Godwin-Austen (1908) , 2 Laidlaw (1933), 3 Solem (1966), 4 Maneevong (2000), 5 Sutcharit and Panha (2008), 6 Pholyotha et al. (2018), 7 Pholyotha et al. (2020a), 8 Pholyotha et al. (2020c), 9 this study. Maximum number of whorls whorl < 5 5 ≤ whorl < 6.
Acknowledgements We thank members of  Animal Systematics Research Unit (ASRU) ,  Department of Biology, Faculty of Science, Chulalongkorn University , Plant Genetic Conservation Project under the  Royal Initiative of Her Royal Highness Princess Maha Chakri Sirindhorn , and the  Royal Thai Navy  for their helping in field collecting, suggestions and technical supports. We are also indebted to  J. Gerber  ( FMNH, Chicago ),  P. Bouchet ,  V. Héros ,  D. Brabant ,  M. Caballer , and  P. Maestrati  ( MNHN, Paris ),  J. Ablett ,  F. Naggs , and  H. Taylor  ( NHM, London ),  H. Wood  ( NMW, Cardiff ),  R. Janssen ,  K. -O. Nagel , and  S. Hof  ( SMF, Frankfurt ) for allowing the authors to examine the collections and photographs. We are grateful for the financial support that we received from the  Thailand Research Fund ,  TRF-DPG628001 , and additionally supported from the  Human Resource Development in Science Project (Science Achievement Scholarship of Thailand, SAST) , the 90 th  Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund) ,  Center of Excellent on Biodiversity  ( BDC-PG2-161002 ), and the  Overseas Research Experience Scholarship for Graduate Students  to AP.
An additional background comes from intrinsic 85 Kr, which is subdominant due to its removal via cryogenic distillation  [57, 58]. The shape of these spectra, particularly at low energies, can be affected by atomic screening and exchange effects, as well as by nuclear structure  [59, 60]. The β decays of 214 Pb and 85 Kr are first forbidden non-unique and first forbidden unique transitions, respectively; however spectra from the IAEA LiveChart (Nuclear Data Services database)  [61]  are based on calculations of allowed and forbidden unique transitions, neither of which includes exchange effects  [62]. Likewise, models from GEANT4  [63]  include only the screening effect; however, its implementation displays a non-physical discontinuity at low energies  [62, 64]. For this work, we performed dedicated theoretical calculations to account for possible low-energy discrepancies from these effects in 214 Pb and 85 Kr spectra.
7 ± 0. 3 events/(t•y•keV). This background is modeled by a fixed, flat component in the fit. One of the continuous backgrounds considered was 136 Xe, a 2νββ emitter intrinsic to xenon. This component has an increasing rate as a function of energy over the ROI. It was constrained in the fit according to the predicted rate and associated uncertainties on (1) a 136 Xe isotopic abundance of (8. 49 ± 0. 04 stat ± 0. 13 sys )% as measured by a residual gas analyzer  [69] , (2) the reported half-life  [70] , and (3) the calculated theoretical spectrum  [71, 72]. The first observation of two-neutrino double electron capture (2νECEC) of 124 Xe was recently reported using mostly the same SR1 dataset (but different selection cuts) as used in this analysis  [73]  and is treated as a background here. In  [73]  we considered the dominant branching ratio of 2νECEC, the capture of two K-shell electrons inducing a peak at 64.
3 keV. It is also possible to capture a K-shell and L-shell electron (36. 7 keV) or two L-shell electrons (9. 8 keV) with decreasing probability, as calculated in  [74]. For this analysis, the event selection and consideration of time dependence allow us to include all three peaks in the background model. The predicted rates of the peaks are taken from an updated half-life  [   with fixed branching ratios from  [74] ; the overall rate was not constrained in the fit since the half-life was derived from the same dataset. Three additional backgrounds were included for neutron-activated isotopes: 133 Xe (β), 131m Xe (internal conversion (IC)), and 125 I (electron capture (EC)). These isotopes were produced during neutron calibrations and decayed away with half-lives of O(10) days. The IC decay of 131m Xe produces a mono-energetic peak at 164 keV  [76] , which, along with the other mono-energetic backgrounds, has the same signature as a bosonic dark matter signal.
f b and f s are the background and signal probability distribution functions, and index j runs over all the background components. C µ and C θ are constraints on the expected numbers of background events and the shape parameters. Index m runs over backgrounds including 85 Kr, solar neutrino, 136 Xe, 83m Kr, 125 I, 133 Xe, and 131m Xe, while index n is for all six shape parameters. Due to time-dependent backgrounds, the SR1 data set is divided into two partitions: SR1 a consisting of events within 50 days following the end of neutron calibrations and SR1 b containing the rest, with effective live times of 55. 8 and 171. 2 days, respectively. Including this time information allows for better constraints on the time-independent backgrounds and improves sensitivity to bosonic dark matter, especially as the time-dependent background from 133 Xe impacts a large fraction of its search region.
e. , S1s (S2s) that do not have a corresponding S2 (S1)  [54]. Surface backgrounds have a strong spatial dependence  [54]  and are removed by the fiducialization (1. 0 tonne here vs. 1. 3 tonnes in  [3] , corresponding to a radial distance from the TPC surface of 11 cm) along with the stricter S2 threshold cut. Both of these backgrounds also have well-understood signatures in the (cS1, cS2 b ) parameter space that are not observed here, as shown in Fig. 5. The detection and selection efficiencies were verified using 220 Rn calibration data. The β decay of 212 Pb, a daughter of 220 Rn, was used to calibrate the ER response of the detector, and thus allows us to validate the efficiency modeling with a high-statistics data set. Similarly to 214 Pb, the model for 212 Pb was calculated to account for atomic screening and exchange effects, as detailed in Appendix A.
With the excess most promi-nent between 2 and 3 keV, where the respective detection efficiencies are ∼ 80% and 94%, changing the analysis threshold has little impact unless set high enough so as to remove the events in question. This is not wellmotivated, given the high efficiency in the region of the excess. For all thresholds considered (namely, 1. 0, 1. 6, 2. 0, 3. 0 keV), the solar axion model gives the best fit to the data. We hence conclude that our choice of analysis threshold impacts neither the presence nor interpretation of the low-energy excess. We also checked data from Science Run 2 (SR2), an R&D science run that followed SR1, in an attempt to understand the observed excess. Many purification upgrades were implemented during SR2, including the replacement of the xenon circulation pumps with units that (1) are more powerful, leading to improved purification speed, and (2) have lower 222 Rn emanation, leading to a reduced 214 Pb background rate in the TPC  [129, 130] , which is further decreased by online radon distillation.
The resulting increased purification speed and reduced background make SR2 useful to study the tritium hypothesis. If the excess were from tritium (or another nonnoble contaminant), we would expect its rate to decrease due to the improved purification; on the other hand, the rate of the signal hypotheses would not change with purification speed. While the SR2 purification upgrades allowed for an improved xenon purity and a reduced background level, the unavoidable interruption of recirculation for the upgrades also led to less stable detector conditions. Thus, in addition to a similar event selection process as SR1 in Sec. III A, we removed several periods of SR2 for this analysis to ensure data quality. Periods where the electron lifetime changed rapidly due to tests of the purification system were removed to reduce uncertainty in the energy reconstruction.