MétaCan
Menu
← Back to cohort
Record W3207353215 · doi:10.1093/mnras/stab2938

Pre-supernova feedback mechanisms drive the destruction of molecular clouds in nearby star-foing disc galaxies

2022· article· en· W3207353215 on OpenAlexafffund
Mélanie Chevance, J. M. Diederik Kruijssen, Mark R. Krumholz, Brent Groves, Ben Keller, Annie Hughes, Simon C. O. Glover, Jonathan D. Henshaw, Cinthya N. Herrera, Jaeyeon Kim, Adam K. Leroy, J. Pety, Alessandro Razza, Erik Rosolowsky, Eva Schinnerer, Andreas Schruba, Ashley T. Barnes, Frank Bigiel, Daniel A. Dale, Éric Emsellem, Christopher M. Faesi, Kathryn Grasha, Ralf S. Klessen, Kathryn Kreckel, Daizhong Liu, Steven N. Longmore, Sharon E. Meidt, Miguel Querejeta, Toshiki Saito, Jiayi Sun, A. Usero

Bibliographic record

VenueUWA Profiles and Research Repository (UWA) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Alberta
FundersH2020 European Research CouncilInstituto Nacional del CáncerNational Institutes of Natural SciencesInstitut national des sciences de l'UniversAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaCollege of Natural Resources and Sciences, Humboldt State UniversityNational Astronomical Observatory of JapanMinisterio de Ciencia e InnovaciónAlberta Livestock and Meat AgencyCalifornia Earthquake AuthorityEuropean School of OncologyDeutscher Akademischer AustauschdienstHorizon 2020 Framework ProgrammeMinistry of Science and TechnologyCentre National d’Etudes SpatialesNational Science FoundationU.S. Nuclear Regulatory CommissionAeroDynamic SolutionsDeutsche ForschungsgemeinschaftKorea Astronomy and Space Science InstituteNational Aeronautics and Space Administration
KeywordsPhysicsStar formationAstrophysicsGalaxyMolecular cloudSupernovaAccretion (finance)StarsMilky WayDisc galaxyAstronomyInitial mass functionGalaxy formation and evolution

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations123
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueUWA Profiles and Research Repository (UWA)→Same topicAstrophysics and Star Formation Studies→French-language works237,207→