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Record W6969135411 · doi:10.5445/ir/1000085424

Search for High-energy Neutrinos from Binary Neutron Star Merger GW170817 with ANTARES, IceCube, and the Pierre Auger Observatory

2017· article· en· W6969135411 on OpenAlexfundno aff

Bibliographic record

VenueRepository KITopen (Karlsruhe Institute of Technology) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersOffice of Polar ProgramsFP7 People: Marie-Curie ActionsInstitut National de Physique Nucléaire et de Physique des ParticulesFundação para a Ciência e a TecnologiaAgencia Estatal de InvestigaciónGovern de les Illes BalearsMarsden FundHelmholtz Alliance for Astroparticle PhysicsIstituto Nazionale di Fisica NucleareMinistério da Ciência, Tecnologia, Inovações e ComunicaçõesXunta de GaliciaConseil Régional Provence-Alpes-Côte d'AzurRussian Science FoundationUniversidad Nacional Autónoma de MéxicoIstituto Nazionale di AstrofisicaEuropean Regional Development FundICTP South American Institute for Fundamental ResearchCentre National de la Recherche ScientifiqueMinistry of Education, IndiaScottish Funding CouncilKnut och Alice Wallenbergs StiftelseFonds De La Recherche Scientifique - FNRSConselho Nacional de Desenvolvimento Científico e TecnológicoMinistero dell’Istruzione, dell’Università e della RicercaConseil Régional d'AlsaceHungarian Scientific Research FundGeneralitat ValencianaDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y TecnologíaComunidad de MadridFonds Wetenschappelijk OnderzoekIndustry CanadaRussian Foundation for Basic ResearchEuropean CommissionLeverhulme TrustFundação de Amparo à Pesquisa do Estado de São PauloDivision of Human Resource DevelopmentMinistero dello Sviluppo EconomicoDanmarks GrundforskningsfondVlaamse regeringNemzeti Kutatási Fejlesztési és Innovációs HivatalNational Research FoundationJavna Agencija za Raziskovalno Dejavnost RSScience and Technology Facilities CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAustralian Research CouncilWestern Canada Research GridGrainger FoundationKavli FoundationAgence Nationale de la RechercheCouncil of Scientific and Industrial Research, IndiaAbdus Salam International Centre for Theoretical PhysicsDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekScience and Engineering Research BoardPolarforskningssekretariatetScottish Universities Physics AllianceNational Natural Science Foundation of ChinaNational Research Foundation of KoreaJapan Society for the Promotion of ScienceVillum FondenUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiFinanciadora de Estudos e ProjetosVetenskapsrådetFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroU.S. Department of EnergyUniversity of Wisconsin-MadisonUniversity of PennsylvaniaNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y CompetitividadBundesministerium für Bildung und ForschungAutoritatea Natională pentru Cercetare StiintificăStichting voor Fundamenteel Onderzoek der MaterieNational Research, Development and Innovation OfficeMinisterie van Onderwijs, Cultuur en WetenschapInstitut des Origines de LyonCanadian Institute for Advanced ResearchRoyal SocietyBelgian Federal Science Policy OfficeNational Science FoundationCompute Canada
KeywordsNeutron starPierre Auger ObservatoryObservatoryNeutrinoBinary starBinary number

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.224
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractno

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