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Record W2017609025 · doi:10.1051/0004-6361/201322489

The applicability of far-infrared fine-structure lines as star formation rate tracers over wide ranges of metallicities and galaxy types

2014· article· en· W2017609025 on OpenAlexaff
Ilse De Looze, D. Cormier, V. Lebouteiller, S. C. Madden, M. Baes, G. J. Bendo, M. Boquien, Alessandro Boselli, D. L. Clements, L. Cortese, Asantha Cooray, M. Galametz, F. Galliano, J. Graciá‐Carpio, K. G. Isaak, O. Ł. Karczewski, T. J. Parkin, E. Pellegrini, A. Rémy-Ruyer, L. Spinoglio, M. W. L. Smith, E. Sturm

Bibliographic record

VenueAstronomy and Astrophysics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
FundersNational Astronomical Observatories, Chinese Academy of SciencesScience and Technology Facilities CouncilMax-Planck-Institut für AstronomieVlaamse regeringCentre National d’Etudes SpatialesBundesministerium für Verkehr, Innovation und TechnologieCentre National de la Recherche ScientifiqueKU LeuvenAgence Nationale de la RechercheFonds Wetenschappelijk OnderzoekNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyImperial College LondonUK Space AgencyCardiff University
KeywordsPhysicsAstrophysicsGalaxyStar formationMetallicityLuminosityInfraredLuminous infrared galaxyDwarf galaxyLine (geometry)AstronomyFar infrared

Abstract

fetched live from OpenAlex

Aims. We analyze the applicability of far-infrared fine-structure lines [Cii] 158 μm, [Oi] 63 μm, and [Oiii] 88 μm to reliably trace the star formation rate (SFR) in a sample of low-metallicity dwarf galaxies from the Herschel Dwarf Galaxy Survey and, furthermore, extend the analysis to a broad sample of galaxies of various types and metallicities in the literature.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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

Citations460
Published2014
Admission routes1
Has abstractyes

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