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Record W1490156253 · doi:10.1051/0004-6361/201425013

Are long gamma-ray bursts biased tracers of star formation? Clues from the host galaxies of the<i>Swift</i>/BAT6 complete sample of LGRBs

2015· article· en· W1490156253 on OpenAlexfundno aff

Bibliographic record

VenueAstronomy and Astrophysics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsnot available
FundersBrookhaven National LaboratoryLeibniz-GemeinschaftScience and Technology Facilities CouncilJet Propulsion LaboratoryOffice of ScienceYork UniversityIstituto Nazionale di AstrofisicaGeneralitat ValencianaDeutsche ForschungsgemeinschaftAgence Nationale de la RechercheAustralian Research CouncilSpace Telescope Science InstituteInstituto de Astrofísica de CanariasCarnegie Mellon UniversityUniversity of ArizonaCollege of Engineering, Michigan State UniversityPrinceton UniversityAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityHarvard UniversityOhio State UniversityNew Mexico State UniversityUniversity of PortsmouthYale UniversityVanderbilt UniversityNational Science FoundationLabex UnivEarthSU.S. Department of EnergyCalifornia Institute of TechnologyComisión Nacional de Investigación Científica y TecnológicaNational Aeronautics and Space Administration
KeywordsGalaxyStar formationStarsStellar massRedshiftMetallicityPopulation

Abstract

fetched live from OpenAlex

Aims. Long gamma-ray bursts (LGRBs) are associated with massive stars and are therefore linked to star formation. However, the conditions needed for the progenitor stars to produce LGRBs can affect the relation between the LGRB rate and star formation. By using the power of a complete LGRB sample, our long-term aim is to understand whether such a bias exists and, if it does, what its origin is.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.003
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.221
Teacher spread0.197 · 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

Citations132
Published2015
Admission routes1
Has abstractyes

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