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Record W2083214570 · doi:10.1086/345384

Uncovering Additional Clues to Galaxy Evolution. I. Dwarf Irregular Galaxies in the Field

2003· article· en· W2083214570 on OpenAlexaff
Henry Lee, M. L. McCall, R. L. Kingsburgh, R. W. Ross, C. C. Stevenson

Bibliographic record

VenueThe Astronomical Journal · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMemorial University of NewfoundlandYork University
Fundersnot available
KeywordsMetallicityAstrophysicsPhysicsDwarf galaxyGalaxyIrregular galaxyLuminosityDwarf galaxy problemGalaxy formation and evolutionAstronomyInteracting galaxy

Abstract

fetched live from OpenAlex

In order to recognize environmental effects on the evolution of dwarf galaxies in clusters of galaxies, it is first necessary to quantify the properties of objects which have evolved in relative isolation. With oxygen abundance as the gauge of metallicity, two key diagnostics of the evolution of dwarf irregular galaxies in the field are re-examined: the metallicity-luminosity relationship and the metallicity-gas fraction relationship. Gas fractions are evaluated from the masses of luminous components only, i.e., constituents of the nucleogenetic pool. Results from new optical spectroscopy obtained for H II regions in five dwarf irregular galaxies in the Local Volume are incorporated into a new analysis of field dwarfs with [O III]4363 detections and good distances. The updated fit to the metallicity-luminosity relationship is consistent with results reported in the literature. The fit to the metallicity-gas fraction relation shows an excellent correlation consistent with expectations of the simple "closed box" model of chemical evolution. The simplest explanation consistent with the data is that flow rates are zero, although the observations allow for the possibility of modest flows. The derived oxygen yield is one-quarter of the value for the solar neighbourhood.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.223
Teacher spread0.215 · 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 teacher head, not a consensus.

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

Citations116
Published2003
Admission routes1
Has abstractyes

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