dataesr/scientific-paragraphs-categorization huggingface dataset:
Fire database and BA thresholds We use the national georeferenced fire database called Prométhée (Prométhée, 2016), which contains information on more than 106 000 wildfires. It corresponds to wildfire characteristics recorded by fire crews and foresters after each intervention from 1973 to 2016. Prométhée gives standardized information on each fire, including the day and hour of ignition, its location on a 2 × 2 km grid (with a total of 20 142 pixels), and the final BA in hectares. Figure  2  presents the temporal evolution of the number of large fires (> 100 ha) for each pyroclimatic region. Clearly, the fire activity is the highest in PCr-1, and PCr-3 has a limited number of large fires in comparison. From these time series, it is also evident that the fire policy set up in 1994 reduced the number of large fires in all regions.
Second, numerous geophysical applications show that, generally, ξ lies within this interval. -The joint prior distribution is (θ ) = (µ) × (log σ ) × (ξ ); i. e. , the prior distributions are considered independent, which is usual in the absence of a priori information about some type of dependence among the GEV parameters (see, e. g. ,  Coles, 2001, p. 174). The posterior distribution (θ |D) can be sampled using a Markov chain Monte Carlo (MCMC) algorithm  (Gilks et al. , 1995; Robert and Casella, 2004). Here, we choose to apply the Metropolis-Hastings algorithm  (Metropolis et al. , 1953)  using the function MCMCmetrop1R from the MCMCpack package in R software  (R Core Team, 2017). In this algorithm, candidates are randomly generated from a proposal distribution and accepted as a new sample according to an acceptance rate, which is computed using the posterior distribution (θ |D).
The main difficulty with the Metropolis-Hastings algorithm is to find an adequate proposal distribution, leading to a reasonable acceptance rate. In this work, the multivariate normal proposal distribution is scaled using the covariance matrix of the parameter estimates when the maximum-likelihood method is applied, using the function fgev from the evd package in R. For each estimation, we produce a burn-in sample size of 100 000, and the retained sample used to represent the posterior distribution is M = 10 000, for which a thinning interval of 10 is applied in order to reduce the autocorrelation inherent in MCMC chains produced with the Metropolis-Hastings algorithm. Draws from the posterior distribution (θ|D) can be used to estimate other unknown quantities. Specifically, the predictive distribution of a quantity y = g(•|θ ), function of some arguments and depending on the GEV parameters θ, is expressed as (see  Gelman et al.
In mid-elevation hinterland (PCr-2) and in mountains (PCr-3), only a dozen fires larger than 100 ha were recorded each year on average since the 1990s. The ongoing climate changes favor an extension of areas with high fire weather danger towards the north and in the mountains  (Dupire et al. , 2017) , and the season favorable to fire activity becomes longer. However, ignitions remain limited and fuels are rarely fully dry. This limits the probability of having very large fires. In these regions, the aerial surveillance by air tackers has been increased in order to detect fires as soon as possible. Finally, it must be remembered that, in this study, we use the occurrence and BA of very large fires as a surrogate for extreme fire danger because no reliable and exhaustive database provides information on the real impacts of fire.
This deserves discussion. On average, the largest fires actually have huge impacts on humans (including fire crews) and the environment because they are likely to have an effect on a large number of assets  (Fernandes et al. , 2016). This is especially true along the Mediterranean coastline, which is densely populated, densely urbanized, and very attractive for tourists. In addition, the largest fires need high expenditure to be suppressed  (Lahaye et al. , 2018). In this context, BA is likely the most appropriate surrogate for extreme fire events. However, a perspective is to better estimate their impacts by comparing the pre-and post-fire biomass in ecosystems and by building databases dedicated to impacts on people and infrastructures.
An ABM is the method of choice when a dynamic process that leads to the formation of an emergent pattern is to be investigated  [18]. This task is different from classification and regression problems, where methods such as random forests may be used to analyze (large) datasets for correlations between certain outcomes, on the one hand, and certain features of the data, on the other hand. The difference is that in the first case the pattern is not known in advance whereas in the latter case the possible outcomes are known and the features are given, even if the dataset changes over time. ABMs are often used to analyze various concepts in science because they account for the connection between autonomous actions of individual scientists and the overall scientific progress  [19]. For example, Gilbert (1997) simulated the growth of scientific publications and distribution of citations per publication as the collective phenomena using the publications themselves as agents  [20] ,  Weisberg and Muldoon (2009)  investigated the collective ability of a research community to discover scientific knowledge  [21] , and  Balietti et al.
Model description General. The model was written in the Python programming language, version 3. 5 (Python Software Foundation, https://www. python. org/), data processing and statistical analysis was done in R  [23]. The code and input data are freely available on https://github. com/ nicolesanikast/ABM_science. The model description follows the ODD (Overview, Design Concepts, Details) protocol  [24, 25]. Here we present the main modeling concepts; the full ODD description is available in section A of the S1 File. Overview. Purpose. The model is an agent-based model aimed at investigating whether the FBMC dynamics can account for the observed imbalance in the choice of materials. To this end, the model simulates the evolution of this research field between 1990 and 2015 using the following processes: (i) formation of collaboration groups; (ii) exchange between agents within collaboration groups; and (iii) choice of materials for study by each collaboration group.
We also ran the model with larger numbers of materials that are available for study and found that this does not change the behavior of the model; see details provided in section B of the S1 File. The simulation starts with a single collaboration group, which corresponds to the empirical number of new researchers entering the experimental field in 1990 (see S1 File for more details). Input data. Input values are calculated from the empirical data for the years 1990-2015: (i) the overall number of PM and DOM types that were studied (94 PM types and 133 DOM types; see Sani-Kast et al. (2017)  [11]  for a description of how the PM and DOM types were defined); (ii) the number of new researchers in each year between 1990 and 2015; (iii) a vector of probabilities for duration of activity (in years).
(2017)  [11]  for details on the selection of the publications and the experiments reported in the publications). The bibliographic data of these publications was obtained from the Web of Science (https://apps. webofknowledge. com, accessed on 23. 02. 2017). By means of the VOSviewer software  [26] , the bibliographic data were converted, without further processing, to a co-authorship (i. e. , collaboration) network. Ensemble of simulated networks. Because of the inherent stochasticity in the simulation, both in the collaboration group formation and in the choice of materials (see section A. 3 in the S1 File), simulation outputs differ in each model run. To account for this randomness, we performed 1000 model runs for different values of the control parameter C. Properties of the empirical collaboration network, empirical experimental network, and the empirical time trend in D comb were compared to the distribution of these properties as obtained for the simulated collaboration networks, the simulated experimental networks, and the simulated time trends in the D comb values, respectively.
Acknowledgments We thank  Eli Goldberg  for valuable comments. Funding by the  Swiss Federal Office for the Environment (BAFU)  is gratefully acknowledged. This work is part of the  NANOHETER  project under the frame of SIINN, the  ERA-NET for a Safe Implementation of Innovative Nanoscience and Nanotechnology. Martin Scheringer  acknowledges financial support by the  Czech Ministry of Education, Youth, and Sports  ( LM2015051 ) and  Masaryk University  (CETOCOEN  PLUS  project,  CZ. 02. 1. 01/0. 0/0. 0/15_003/0000469 ).
This work was supported by the  Swiss Federal Office for the Environment (BAFU) :  Nanoheter  ( Dr. Nicole Sani-Kast ) SINN, the  ERA-NET for a Safe Implementation of Innovative Nanoscience and Nanotechnology , http://www. siinn. eu/( Dr. Nicole Sani-Kast ). This work was also supported by the  Czech Ministry of Education , Youth, and Sports ( LM2015051 ) ( Martin Scheringer )  Masaryk University  (CETOCOEN  PLUS  project,  CZ. 02. 1. 01/0. 0/0. 0/15_003/0000469 )( Martin Scheringer ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction 9] [60] [61] [62] [63] [64]  Such antigens are commonly known as "tumor-associated antigens" (TAAs). 55, 65 A large list of TAAs with sequences that bind human MHC Class I or II molecules and the TCR can be found at http://cvc. dfci. harvard. edu/tadb (the Tantigen database). 7] [78]  Similarly, TNAs that are fully tumor-specific occur upon the rearrangement of immunoglobulin-coding genes in clonal B-cell malignancies. 79 Finally, tumor-specific TAAs can be generated as a consequence of viral transformation,  80  as in the case of human papillomavirus type 16 (HPV-16)-driven oral and cervical tumors. 81, 82 9] [120] [121]  In line with this notion, no peptide-based vaccines are currently approved by the US Food and Drug Administration (FDA) or equivalent agencies worldwide for use in cancer patients are therapeutic measures (source http://www.
Ongoing clinical trials Since the last Trial Watch dealing with peptide-based vaccines for oncological indications has been published (April 2015),  118  no less than 66 clinical trials have been initiated to test this immunotherapeutic modality in cancer patients (source www. clinicaltrials. gov) (Table  1 ). 2] [223] [224]  In line with preclinical and clinical data demonstrating that multi-epitope vaccines are generally more powerful than their singleepitope counterparts,  117, 225  the most common vaccination strategy employed by these studies consists in targeting simultaneously multiple TAAs (20 studies). Alongside, 15 studies are investigating the safety and efficacy of PPV, often consisting of MHC-matched peptides chosen from the immune repertoire of the patient before treatment. 226 4] [255] [256]  Taken together, these clinical trials enroll patients with a wide panel of neoplasms, including (but not limited to) glioblastoma, glioma and other brain tumors (NCT02722512; NCT02924038; NCT03068832; NCT03299309; NCT02750891; NCT03149003; NCT02287428; NCT02455557; NCT02754362; NCT02864368; NCT03223103; NCT03422094; NCT02358187; NCT02454634; NCT02960230), breast carcinoma (NCT02276300; NCT025932 27; NCT02636582; NCT03012100; NCT02826434; NCT0336 2060), hematological malignancies (NCT02240537; NCT02802943; NCT03219450; NCT02396134; NCT02750995; NCT03121677; NCT03361852; NCT03381768; NCT02436252), melanoma (NCT02320305; NCT02334735; NCT02382549; NCT02385669; NCT02425306; NCT02515227; NCT02696356; NCT03047928), ovarian carcinoma (NCT02764333; NCT02978 222; NCT02737787; NCT02933073) and prostate cancer (NCT03412786; NCT02293707; NCT02452307; NCT03199872).
Although final statistical assessments are still awaited, preliminary results from 8 clinical trials that have been completed or terminated since the publication of our last Trial Watch dealing with peptide-based anticancer vaccines (April 2015)  118  have become available (source www. clinicaltrials. gov). NCT01423760, an open-label, common safety follow-up trial testing a MUC1-targeting vaccine (tecemotide) in patients with myeloma and NSCLC has been terminated prematurely as per decision of the sponsor. Out of 27 patients enrolled in the study, 20 were evaluable for toxicity, which was more severe in the NSCLC arm. NCT00409188, a Phase III study testing tecemotide in combination with single low-dose cyclophosphamide in subjects with NSCLC has been completed. Primary endpoint was not met, but notable survival benefits were achieved in patients treated with concurrent chemoradiotherapy, 257 NCT01507103, a Phase II study testing the therapeutic profile of tecemotide combined with cyclophosphamide or cyclophosphamide plus chemoradiation in subjects with rectal cancer, has been completed.
No difference in incidence and severity of adverse events were noted. NCT01380145, an open-label, single-arm, pilot study of recombinant MAGEA3 adjuvanted with AS15  258  as consolidation for multiple myeloma patients undergoing autologous stem cell transplantation, has been completed. Treatment was immunologically active, but grade 3-4 adverse events were experienced by 12 of the 13 participants in the study. One year after treatment there were 4 patients in stringent complete response (CR), 1 in CR, 4 in very good partial response (PR) and 4  Abbreviations. CMV, cytomegalovirus; CRC, colorectal carcinoma; DC, dendritic cell; HCC, hepatocellular carcinoma; HPV, human papillomavirus; MDS, myelodysplastic syndrome; n. a. , not available; NSCLC, non-small cell lung carcinoma; PPV, personalized peptide vaccination, SLP, synthetic long peptide; TAA, tumor-associated antigen.
with progressive disease. NCT00849875, a Phase II study testing MUC1-targeting vaccination plus dacarbazine in melanoma patients, has been terminated due to lack of scientific justification to continue collect data. Of 48 participants analyzed, 10 had serious adverse events. Seroconversion occurred in all patients, but clinical activity was limited to 1 CRs and 3 PRs. NCT00706992, a Phase 2 trial testing a peptide-based vaccine specific for melan-A (MLANA; also known as MART-1) together with MART-1-targeting lymphocytes in high-risk melanoma patients, has been terminated owing to low accrual. No robust immunological responses were documented among 40 evaluable patients. Adverse events were common, but never serious. NCT01322815, a Phase II study assessing the therapeutic profile of a peptide-based vaccine targeting mutant KRAS combined with standard chemotherapy or a mAb specific for vascular endothelial growth factor A (VEGFA)  259  in patients with colorectal carcinoma, has been terminated owing to poor accrual rate.
Four months after the initiation of treatment, 50% of patients were alive and free of progression, but 2 patients receiving GI-4000 plus chemotherapy suffered from serious adverse effects. NCT00643097, a Phase I-II trial investigating the safety and preliminary therapeutic profile of an EGFRvIIIdirected vaccine adjuvanted with GM-CSF in patients with glioblastoma, has been completed. Of 30 participants evaluable for the immunogenicity of the vaccine, 10 presented robust immune responses, median progression-free survival was between 11. 6 and 14. 2 months. NCT01307618, a Phase II study testing a multi-epitope peptide-based vaccine in combination with a CD25-specific antibody (daclizumab) ± recombinant metastatic interleukin 12 (IL12) in patients with metastatic melanoma, was terminated due to lack of efficacy.
Acknowledgments LB is supported by  Bristol-Myers Squibb Foundation for Research in Immuno-Oncology (BMS). GK is supported by the  Ligue contre le Cancer  (équipe labelisée);  Agence National de la Recherche (ANR) -Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Fondation pour la Recherche Médicale (FRM) ;  Cancéropôle Ile-de-France ;  Institut National du Cancer (INCa) ;  Institut Universitaire de France ; the  European Commission (ArtForce) ; the  European Research Council (ERC) ; the  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumière , the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; the  SIRIC Cancer Research and Personalized Medicine (CARPEM) ; and the  Paris Alliance of Cancer Research Institutes (PACRI).
Thermo-mechanical model Our thermo-mechanical ice-flow model is based on the Stokes equation coupled with an energy equation using the enthalpy formulation  (Aschwanden et al. , 2012; Gilbert et al. , 2014). Changes in the glacier geometry are computed using a free surface equation  (Gilbert et al. , 2014). We adopt a pure viscous isotropic ice rheology following Glen's flow law  (Cuffey and Paterson, 2010). The model is solved using the finite-element software Elmer/Ice  (Gagliardini et al. , A. Gilbert et al. :  Mechanisms leading to the 2016 giant twin glacier collapses   2. We adopt a linear friction law as a basal boundary condition for the Stokes equation: τ b = βu s , (1) where τ b is the basal shear stress (MPa), u s is the sliding velocity (m yr -1 ), and β is the friction coefficient (MPa yr m -1 ).
Data availability. The list of satellite data used is included in the Table  1. We also used Sentinel data in addition to compute velocities. Sentinel-1 and 2 are publicly available from https://scihub. copernicus. eu/ (last access: 2017). Access to all TanDEM-X data can be applied online from DLR (https://tandemx-science. dlr. de/, last access: 2017). Spot and Pléiades are commercial data which can be purchased from Airbus Defense and Space (https://www. intelligence-airbusds. com/geostore/, last access: 2017). Academic access at reduced cost exists (http://www. satelliteimageaccess. teledetection. fr/, last access: 2017). SRTM-C data are available from http://earthexplorer. usgs. gov (last access: 2017). WorldView data are commercial. Any other specific data can be provided by the authors on request.
Context. Super-luminous supernovae (SLSNe) are rare events defined as being significantly more luminous than normal terminal stellar explosions. The source of the additional power needed to achieve such luminosities is still unclear. Discoveries in the local Universe (i. e. z < 0. 1) are scarce, but afford dense multi-wavelength observations. Additional low-redshift objects are therefore extremely valuable. Aims. We present early-time observations of the type I SLSN ASASSN-18km/SN 2018bsz. These data are used to characterise the event and compare to literature SLSNe and spectral models. Host galaxy properties are also analysed. Methods. Optical and near-IR photometry and spectroscopy were analysed. Early-time ATLAS photometry was used to constrain the rising light curve. We identified a number of spectral features in optical-wavelength spectra and track their time evolution.
SN 2018bsz exploded in the galaxy 2MASX J16093905-3203443 and is affected by Milky Way line of sight extinction of E(B-V)=0. 214 mag  (Schlafly & Finkbeiner 2011 ) assuming a  Fitzpatrick (1999)  reddening law and an R V of 3. 1. Throughout the paper we assume an H 0 of 73 km s -1 Mpc -1 and a standard cosmology (Ω m = 0. 27, Ω λ = 0. 73;  Spergel et al. 2007 ). We present UV, optical and near-IR photometry and spectroscopy of SN 2018bsz ranging from 50 days before to a week past maximum optical light. These data are compared to other well-observed events, and are used to characterise the overall behaviour of SN 2018bsz in the context of our current understanding of SLSNe. In the next section we summarise the discovery, classification and reclassification of SN 2018bsz. In Section 3 we present our spectroscopy.
Data from the UV to the near-IR were obtained from the Galaxy Evolution Explorer (GALEX;  Martin et al. 2005) , the Two Micron All-Sky Survey (2MASS;  Huchra et al. 2012 ) and the Wide-field Infrared Survey Explorer (WISE;  Wright et al. 2010 ) public databases. Skymapper does not report catalogue photometry for extended objects, so we performed our own photometry on the SkyMapper images using an aperture radius of 29 pixels (14. 4 ; 7. 7 kpc), establishing the photometric calibration by direct comparison to nearby stars in the SkyMapper catalogue  (Wolf et al. 2018). We then fit the broad-band SED of 2MASX J16093905-3203443 using Le Phare  (Ilbert et al. 2006 ) to determine the stellar mass and star-formation rate as in  Taggart et al. (in preparation). The results indicate 2MASX J16093905-3203443 is a star-forming, low-mass galaxy with a moderate amount of dust.
Acknowledgements. We thank the staff at Paranal for their efficiency in obtaining our observations. James Leftley  is thanked for help with the SED fitting of the SN photometry. This work is based (in part) on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere, Chile  as part of ePESSTO, (the extended  Public ESO Spectroscopic Survey for Transient Objects Survey ) programme 199. D-0143. Based on (in part) observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme(s) 2101. D-5023(A). Based on observations obtained at the  Gemini Observatory  under programme GS-2018A-Q-107 (PI: Sand). Gemini is operated by the  Association of Universities for Research in Astronomy, Inc. , under a cooperative agreement with the NSF on behalf of the Gemini partnership: the  NSF (United States ), the  National Research Council (Canada) ,  CONICYT (Chile) ,  Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina) , and  Ministério da Ciência, Tecnologia e Inovação (Brazil).
Part of the funding for  GROND  (both hardware as well as personnel) was generously granted from  the Leibniz-Prize  to  Prof. G. Hasinger  ( DFG grant   HA 1850/28-1 ). SJS acknowledges funding from  STFC  Grant Ref:  ST/P000312/1. GL is supported by a research grant ( 19054 ) from  VILLUM FONDEN. Parts of this research were conducted by the  Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) , through project number  CE110001020. TWC acknowledgments the funding provided by the  Alexander von Humboldt Foundation. Support for this work was provided by  NASA  grant  NN12AR55G. Research by DJS is supported by  NSF  grants  AST-1821987  and  1821967. This work makes use of observations from the  Las Cumbres Observatory network. DAH, CM , and GH are supported by  NSF  grant  AST 1313484.
A. G. -Y. is supported by the  EU  via  ERC  grant No. 725161 , the  Quantum Universe I-Core program , the  ISF , the  BSF Transformative program  and by a  Kimmel award. Support for IA was provided by  NASA  through the  Einstein Fellowship Program , grant  PF6-170148. LG was supported in part by the  US National Science Foundation  under Grant  AST-1311862. MG is supported by the  Polish National Science Centre  grant  OPUS 2015/17/B/ST9/03167. MF is supported by a  Royal Society -Science Foundation Ireland University Research Fellowship. KM is supported by  STFC  through an  Ernest Rutherford fellowship. MB acknowledges support from the  Swedish Research Council (Vetenskapsrådet)  and the  Swedish National Space Board. AJR is funded by the  Australian Research Council  through grant number  FT170100243.