dataesr/scientific-paragraphs-categorization huggingface dataset:
Hadronic Model From a purely electromagnetic perspective, leptonic radiative models and hadronic radiative models can fit blazar SEDs equally well (see, e. g. , Cerruti 2020). While for luminous blazars hadronic models face the difficulty of requiring often   5. Data with frequencies in the UV or lower are considered as upper limits for the modeling of the blazar emission and are therefore depicted with arrows. For the jetset model (cyan) the confidence interval of the MCMC fit is displayed as the framework allows for it. super-Eddington powers, and can thus be disfavored from an energetic point of view, this is not true for HSP BL Lac type objects, and in particular when low flux states are studied. In this case, leptonic and hadronic emission scenarios are truly degenerate in their photon emission and can only be distinguished via the detection of neutrinos, naturally produced in hadronic interactions while absent in leptonic ones.
Acknowledgments We would like to thank the  Instituto de Astrofísica de Canarias  for the excellent working conditions at the  Observatorio del Roque de los Muchachos in La Palma. The financial support of the  German BMBF, MPG , and HGF; the  Italian INFN  and  INAF ; the  Swiss National Fund (SNF) ; grants  PID2019-104114RB-C31 ,  PID2019-104114RB-C32 ,  PID2019-104114RB-C33 ,  PID2019-105510GB-C31 ,  PID2019-107847RB-C41 ,  PID2019-107847RB-C42 ,  PID2019-107847RB-C44 , and  PID2019-107988GB-C22  funded by  MCIN/AEI/10. 13039/501100011033 ; the  Indian Department of Atomic Energy ; the  Japanese ICRR , the  University of Tokyo ,  JSPS , and the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) ; the  Bulgarian Ministry of Education and Science, National RI   Roadmap  Project  DO1-400/ 18.
This research has made use of the XRT Data Analysis Software (XRTDAS) developed under the responsibility of the  ASI Science Data Center (ASDC), Italy. L. H. acknowledges the support from the 2019  Fermi Summer School  hosted by the  University of Delaware , and the  Fermi Science Support Center  and the lead organizers and instructors  Joe Asercion ,  Erik Blaufuss ,  Regina Caputo ,  Mattia Di Mauro ,  Joe Eggen ,  Manel Errando ,  Henrike Fleischhack ,  Adam Goldstein ,  Colby Haggerty ,  Liz Hays ,  Jamie Holder ,  Julie McEnery ,  Jeremy Perkins ,  Judy Racusin ,  Alex Reustle ,  Jacob Smith , and  Leo Singer. M. C. has received financial support through the  Postdoctoral Junior Leader Fellowship Programme  from  la Caixa Banking Foundation , grant No. LCF/BQ/LI18/11630012. A. A. E. and D.
P. acknowledge support from the  Deutsche Forschungsgemeinschaft (DFG ;  German Research Foundation ) under Germany's Excellence Strategy EXC-2094-390783311. M. B. acknowledges support from the  YCAA Prize Postdoctoral Fellowship  and from the  Black Hole Initiative at Harvard University , which is funded in part by the  Gordon and Betty Moore Foundation  (grant  GBMF8273 ) and in part by the  John Templeton Foundation. The Abastumani team acknowledges financial support by the  Shota Rustaveli NSF of Georgia  under contract  FR-19-6174. The R-band photometric data from the  University of Athens Observatory (UOAO)  were obtained after utilizing the robotic and remotely controlled instruments at the facilities. This research was partially supported by the  Bulgarian National Science Fund of the Ministry of Education and Science  under grants DN  18-10/2017 ,  KP-06-H28/3 (2018 ),  KP-06-H38/4  ( 2019 ), and  KP-06-KITAJ/2 (2020 ) and the  National RI   Roadmap  Project  D01-383/18.
G. D. acknowledges observing grant support from the  Institute of Astronomy and Rozhen NAO BAS  through the bilateral joint research project  Gaia Celestial Reference  Frame (CRF) and fast variable astronomical objects (2020( -2022, head-G. Damljanovic), head-G. Damljanovic). This research was supported by the  Ministry of Education, Science and Technological Development of the Republic of Serbia  (contract No. 451-03-68/2022-14/200002 ). M. D. J. thanks the  Brigham Young University Department of Physics and Astronomy  for continued support of the ongoing extragalactic monitoring program at the  West Mountain Observatory. S. K. acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No.
771282. This research has made use of data from the  OVRO 40 m monitoring program  (Richards, J. L. et al. 2011, ApJS, 194, 29), supported by private funding from the  California Insitute of Technology  and the  Max-Planck Institute for Radio Astronomy , and by  NASA  grants  NNX08AW31G ,  NNX11A043G , and  NNX14AQ89G  and  NSF  grants  AST-0808050  and  AST-1109911. This publication makes use of data obtained at  Metsähovi Radio Observatory , operated by  Aalto University in Finland. The Medicina radio telescope is funded by the  Italian Ministry of University and Research (MUR)  and is operated as a National Facility by the  Italian National Institute for Astrophysics (INAF). The  Submillimeter Array  is a joint project between the  Smithsonian Astrophysical Observatory  and the  Academia Sinica Institute of Astronomy and Astrophysics  and is funded by the  Smithsonian Institution  and the  Academia Sinica.
The  IAA-CSIC  co-authors acknowledge financial support from the  Spanish "Ministerio de Ciencia e Innovació" (MCINN)  through the  "Center of Excellence Severo Ochoa" award  for the  Instituto de Astrofísica de Andalucía-CSIC  ( SEV-2017-0709 ). Acquisition and reduction of the POLAMI data were supported in part by  MICINN  through grants  AYA2016-80889-P  and  PID2019-107847RB-C44. The POLAMI observations were carried out at the IRAM 30 m Telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany) , and  IGN (Spain). This publication makes use of data products from the  Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE ), which is a joint project of the  Jet Propulsion Laboratory/California Institute of Technology  and the  University of Arizona. NEOWISE is funded by the  National Aeronautics and Space Administration.
The OMPS/SAGE datasets are discussed in more details in S22 and are briefly recalled in Sect. 2. 2. As discussed in S22, the interaction between radiation and biomass burning aerosols cannot be fully described by the spectral aerosol extinction, which integrates the absorption and scattering processes without differentiating between them, and without describing the angular distribution of the scattered radiation. The absorption properties of the aerosol layer are particularly important for the estimation of the localised heating/cooling within the plume. Unfortunately, the absorptivity and the scattering angular distribution properties of aerosol are not directly accessible from observations. Thus, we have made different hypotheses on these properties of the simulated smoke plumes, in our radiative transfer calculations.
, 2016). Typical values in the SW, with the Oxford Mie code, using sulphate aerosol refractive indices from  Hummel et al. , 1988  and a mono-modal log-normal size distribution with width of 1. 86 and mean radius from of 0. 2 µm. For the description of the more localised smoke-charged compact anticyclonic vortices, we use high vertical resolution aerosol extinction observations with the satellite-borne LiDAR CALIPSO-CALIOP. The CALIOP datasets are briefly discusses in Sect. 2. 3. As for the hemispheric-scale HR estimations, for the localised in-vortex calculations we have made hypotheses for the non-measured optical properties, SSA and g. More constrains on these optical properties are given by the CALIOP measurements of the LiDAR ratio (LR), colour ratio (CR) and depolarisation ratio (d), which is discussed in Sects.
Aerosol spectral extinction observations with the The main detached vortex is tracked using ECMWF (European Centre for Medium-Range Weather Forecasts)-IFS (Integrated Forecasting System) reanalyses. As done by  Khaykin et al. (2020) , ECMWF-IFS-derived datasets of vorticity and ozone anomalies are used as vortex tracers. In this work, these datasets are used to identify CALIOP overpasses of the main detached vortex from Australian bushfires.
Acknowledgments This research has been supported by the  Agence Nationale de la Recherche (ANR , grant no  21-CE01-0007-01 ,  ASTuS ) and the  Centre National d'Etudes Spatiales (CNES , grant  CNES /TOSCA/ EECLAT ). The providers of the libRadtran suite (http://www. libradtran. org/) are gratefully acknowledged.
Data availability OMPS-LP v2. 0 and SAGE data are freely available via the NASA-Earthdata portal (https://search. earthdata. nasa. gov/search). The CALIOP data v3. 41 are freely available from the NASA LARC website at: https://doi. org/10. 5067/CALIOP/CALIPSO/CAL_LID_L1-VALSTAGE1-V3-41.
Ozone Mapper and Profiler Suite -Limb Profiler (OMPS-LP) and the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS)The spatiotemporal variability of the average large-scale fire-perturbed and the background aerosol extinction is obtained using v2. 0 aerosol extinction observations at 675 nm from the Ozone Mapping and Profiler Suite -Limb Profiler (OMPS-LP), onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite since January 2012(Taha et al. , 2020). The OMPS-LP instrument observes scattered solar radiation at different tangent heights (from cloud-top height to 40 km, with a 1-km vertical resolution), in the 290-1000 nm spectral range. Even if OMPS-LP v2. 0 observes the aerosol extinction at different wavelengths, multi-spectral analyses with this dataset are not recommended due to inhomogeneities of the accuracy of spectral aerosol observations due to different sources of bias for each band(Taha et al.
Article https://doi. org/10. 1038/s41562-022-01517-1 operationalization of expertise). By including social scientists with expertise in different subject matters, we could examine how such expertise may contribute to forecasting accuracy above and beyond general training in the social sciences. The teams were not constrained in terms of the methods used to generate time-point forecasts. They provided open-ended, free-text responses for the descriptions of the methods used, which were coded later. If they used data-driven methods, they also provided the model and any additional data used to generate their forecasts (Methods). We also collected data on team size and composition, area of research specialization, subject domain and forecasting expertise, and prediction confidence. We benchmarked forecasting accuracy against several alternatives.
Results Following the a priori outlined analytic plan (https://osf. io/7ekfm; the details are in the Supplementary Methods) to determine forecasting accuracy across domains, we examined the mean absolute scaled error (MASE)  18  across forecasted time points for each domain. The MASE is an asymptotically normal, scale-independent scoring rule that compares predicted values against the predictions of a one-step random walk. Because it is scale independent, it is an adequate measure when comparing accuracy across domains on different scales. A MASE of 1 reflects a forecast that is as good out of sample as the naive one-step random walk forecast is in sample. A MASE below 1. 76 is superior to median performance in prior large-scale data science competitions  7. See the Supplementary Information for further details of the MASE method.
With the onset of the COVID-19 pandemic as a backdrop, we selected these domains on the basis of data availability and theoretical links to the pandemic. Prior research has suggested that each of these domains may be impacted by infectious disease  [11] [12] [13] [14]  or pandemic-related social isolation  15. To understand how scientists made predictions in these domains, we documented the rationales and processes they used to generate forecasts, and we then examined how different methodological choices were related to accuracy.
Teams could select up to 12 domains to forecast, including domains for which team members reported a track record of peer-reviewed publications as well as domains for which they did not possess relevant expertise (see the Methods for the multi-stage Article https://doi. org/10. 1038/s41562-022-01517-1 five domains. Even winning teams were still less accurate than in-sample random walks for 8 of 12 domains in Tournament 1 and one domain (Republican support) in Tournament 2 (Supplementary Tables  1  and 2  and Supplementary Figs. 4 5 6 7 8 9 ). One should note that inferior performance to the in-sample random walk (MASE > 1) may not be too surprising; errors of the in-sample random walk in the denominator concern historical observations that occurred before the pandemic, whereas the accuracy of scientific forecasts in the numerator concerns the data for the first pandemic year.
However, average forecasting accuracy did not generally beat more liberal benchmarks such as the median MASE in data science tournaments (1. 76)  7  or the benchmark MASE for 'good' forecasts in the tourism industry (Supplementary Information). Except for one team, the top forecasters from Tournament 1 did not appear among the winners of Tournament 2 (Supplementary Tables  1  and 2 ). We examined the accuracy of scientific and lay forecasts in a linear mixed-effect model. To systematically compare results for different forecasted domains, we tested a full model with expertise (social scientist versus lay crowd), domain and their interaction as predictors, and log(MASE) scores nested in participants. We observed no significant main effect difference between the accuracy of social scientists and that of lay crowds (F(11, 1,747) = 0.
Which strategies and team characteristics promoted accuracy? Finally, we examined forecasting approaches and individual characteristics of more accurate forecasters in the tournaments. In the main text, we focused on central tendencies across forecasting teams, whereas in the supplementary analyses we reviewed strategies of winning teams and characteristics of the top five performers in each domain (Supplementary Figs. 4-11). We compared forecasting approaches relying on (1) no data modelling (but possible consideration of theories), (  2.
There were no domains where data-free models were more accurate than data-inclusive models. Analyses further demonstrated that, in the first tournament, data-free forecasts of social scientists were not significantly better than lay estimates (t(577) = 0. 87, P = 0. 385), whereas data-inclusive models tended to perform significantly better than lay estimates (t(470) = 3. 11, P = 0. 006, Cohen's d = 0. 391). To examine the incremental contributions of specific forecasting strategies and team characteristics to accuracy, we pooled data from both tournaments in a linear mixed model with inaccuracy (MASE) as a dependent variable. As Fig. 6  shows, we included predictors representing forecasting strategies, team characteristics, domain expertise (quantified via publications by team members on the topic) and forecasting expertise (quantified via prior experience with forecasting tournaments).
Deviating from the pre-registration, we performed a single analysis with all covariates in the same model rather than performing separate analyses for each set of covariates, to protect against inflating P values. Furthermore, due to scale differences between domains, we chose not to feature analyses concerning absolute percentage errors of each time point in the main paper (but see the corresponding analyses on the GitHub site for the project, https://github. com/grossmania/Forecasting-Tournament, which replicate the key effects presented in the main manuscript).
Analytic plan Categorization of forecasts. We categorized the forecasts on the basis of modelling approaches. Two independent research associates categorized the forecasts for each domain on the basis of the following justifications: (1) theoretical models only, (2) data-driven models only or (3) a combination of theoretical and data-driven models-that is, computational models that rely on specific theoretical assumptions. See Supplementary Appendix 3 for the exact coding instructions and a description of the classification (interrater κ = 0. 81 unweighted, κ = 0. 90 weighted). We further examined the modelling complexity of approaches that relied on the extrapolation of time series from the data we provided (for example, ARIMA or moving average with lags; yes/ no; see Supplementary Appendix 4 for the exact coding instructions).
Categorization of teams. We next categorized the teams on the basis of compositions. First, we counted the number of members per team. We also sorted the teams on the basis of disciplinary orientation, comparing behavioural and social scientists with teams from computer and data science. Finally, we used information that the teams provided concerning their objective and subjective expertise levels for a given subject domain. Forecasting update justifications. Given that the participants received both new data and a summary of diverse theoretical positions that they could use as a basis for their updates, two independent research associates scored the participants' justifications for forecasting updates on three dummy categories: (1) the new six months of data that we provided, (2) new theoretical insights and (3) consideration of other external events (interrater κ = 0.
Acknowledgements This programme of research was supported by the  Basic Research Program  at the  National Research University Higher School of Economics (M. Fabrykant ),  John Templeton Foundation  grant no. 62260  (I. G. and P. E. T. ),  Kega 079UK-4/2021  (P. K. ),  Ministerio de Ciencia e Innovación España  grants no. PID2019-111512RB-I00-HMDM  and no. HDL-HS-280218  (A. A. ), the  National Center for Complementary & Integrative Health of the National Institutes of Health  under award no. K23AT010879  (S. B. G. ),  National Science Foundation RAPID  grant no. 2026854  (M. E. W. V. ),  PID2019-111512RB-I00  (M. S. ),  NPO Systemic Risk Institute  grant no. LX22NPO5101  (I. R. ), the  Slovak Research and Development Agency  under contract no. APVV-20-0319  (M. A. ),  Social Sciences and Humanities Research Council of Canada Insight  grant no.
Data availability All data used in the main text and supplementary analysis are accessible on GitHub (https://github. com/grossmania/Forecasting-Tournament). All prior data presented to the forecasters are available at https:// predictions. uwaterloo. ca/. Historical and ground truth markers were obtained from Project FiveThirtyEight (https://projects. fivethirtyeight. com/polls/generic-ballot), Gallup (https://news. gallup. com/poll/ 203198/presidential-approval-ratings-donald-trump. aspx), Project Implicit (see the Open Science Framework website at https://osf. io/t4bnj) and the US Census Bureau (https://www. census. gov/data/tables/ time-series/demo/popest/2010s-national-detail. html).
Data analysis version 4. 2. 2 (2022-10-31 ucrt) with the following packages: tidyquant_1. 0. 4 quantmod_0. 4. 20 TTR_0. 24. 3 PerformanceAnalytics_2. 0. 4 xts_0. 12. 1 zoo_1. 8-10 ggdist_3. 2. 0 bayestestR_0. 12. 1 rstanarm_2. 21. 3 Rcpp_1. 0. 9 ggpubr_0. 4. 0 moments_0. 14. 1 partR2_0. 9. 1 CGPfunctions_0. 6. 3 tsibble_1. 1. 2 statcomp_0. 1. 0 lubridate_1. 8. 0 Hmisc_4. 7-0 Formula_1. 2-4 survival_3. 4-0 lattice_0. 20-45 ggsci_2. 9 jtools_2. 2. 0 car_3. 1-0 carData_3. 0-5 emmeans_1. 8. 0 lme4_1. 1-30 Matrix_1. 5-1 irr_0. 84. 1 lpSolve_5. 6. 15 forcats_0. 5. 1 stringr_1. 4. 0 dplyr_1. 0. 9 purrr_0. 3. 4 readr_2. 1. 2 tidyr_1. 2. 0 tibble_3. 1. 8 ggplot2_3. 3. 6 tidyverse_1. 3. 2 psych_2. 2. 5 forecast_8. 17. 0 For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers.
We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information. Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy All data used in the main text and supplementary analysis is accessible on GitHub (https://github. com/grossmania/Forecasting-Tournament). All prior data presented to forecasters are available on https://predictions.
Digitalisation and imaging. Photographs were taken by G. D and M. R. CT scan image stacks were acquired using CT scanner Philips IQon Spectral CT (Advanced Diagnostic Imaging Center (AIMC), Mahidol University in Thailand), with voltage of 120 kV and current of 562 µA. A total of 1272 slices with thickness of 0. 80 mm were generated and voxel size of 0. 234375 × 0. 234375 × 0. 4 mm. Digital reconstructions and CT scan image stacks were processed using Amira software 3D 2021. 1 (https:// www. therm ofish er. com/ order/ catal og/ produ ct/ AMIRA) in the 2D/3D imaging/digitisation lab of the Centre of Visualisation, Digitalisation and Replication at the Eberhard Karls Universität Tübingen, Germany. Additional photographs of 3D models were made using the software MeshLab 2021. 07 (https:// www.
Acknowledgements We thank  Christopher Brochu  and an anonymous reviewer for comments and suggestions that improved the quality of the manuscript. We also thank the editor  Michal Zaton  for assistance and discussions. G. D. , M. R. , and K. S. would like to thank  Doungrutai Saesaengseerung  and staff from the  Department of Mineral Resources (Bangkok)  for facilitating their visit of the collection. We are grateful to  Sith Phongkitkarun  (acting director), technicians, and staff from the  Advanced Diagnostic Imaging Center (AIMC) ,  Faculty of Medicine Ramathibodi Hospital (Mahidol University, Bangkok)  for the CT-scan analysis of the specimen. We thank  Frank Glaw  and  Michael Franzen  ( Bavarian State Collection of Zoology in Münich ) and  Erick Weber  ( Zoologische Schausammlung Tübingen ) for providing access to collections under their care.
(2001)  that has inspired various SCF parameterizations adapted to mountainous areas (e. g. ,  Swenson and Lawrence, 2012; Li et al. , 2019; Helbig et al. , 2021). However, it is challenging to develop, calibrate, and evaluate SCF parameterizations over mountainous areas -which represent over 30 % of land areas  (Sayre et al. , 2018; Körner et al. , 2021)  -because of the lack of accurate snow datasets over these areas  (Dozier et al. , 2016; Bormann et al. , 2018). To alleviate this lack of data,  Liu et al. (2021b)  produced the High Mountain Asia Snow Reanalysis (HMASR) using a method validated in the Sierra Nevada  (Margulis et al. , 2016) , in the Andes  (Cortés and Margulis, 2017) , and in the western United States  (Fang et al. , 2022) , consisting in assimilating high-resolution SCF satellite images from MODIS and Landsat to provide posterior estimates of snow-related variables.
Two main questions are approached in this study: (1) does the subgrid topography need to be taken into account in SCF parameterizations? (2) What is the benefit of using SCF parameterizations calibrated over HMA in GCM global experiments? The following secondary questions are also addressed: (3) is it relevant to split the snow accumulation and depletion curves as it is done in SL12? (4) Can machine learning reproduce SCF parameterizations? (5) Does the calibration of SCF parameterizations depend on the spatial resolution? And, (  6 ), what are the land-atmosphere feedbacks induced by SCF changes in model experiments? This article is organized as follows: data and methods are described in Sect. 2. Section 3 presents a skill analysis of HMASR based on in situ SD observations. The evaluation and calibration of the SCF parameterizations with respect to HMASR are described in Sect.