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High-resolution spectroscopic study of red clump stars in the Galaxy: iron-group elements

2010· article· en· W2106745311 on OpenAlexaff
E. Puzeras, G. Tautvaiśienė, Judith G. Cohen, David F. Gray, Saul J. Adelman, I. Ilyin, Y. Chorniy

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsWestern University
Fundersnot available
KeywordsPhysicsAstrophysicsMetallicityRed clumpStarsGalaxyAstronomyRed giantGiant star

Abstract

fetched live from OpenAlex

The main atmospheric parameters and abundances of the iron-group elements (vanadium, chromium, iron, cobalt and nickel) are determined for 62 red giant ‘clump’ stars revealed in the Galactic field by the Hipparcos orbiting observatory. The stars form a homogeneous sample with the mean value of temperature Teff= 4750 ± 160 K, of surface gravity log g= 2.41 ± 0.26 and the mean value of metallicity [Fe/H]=−0.04 ± 0.15 dex. A Gaussian fit to the [Fe/H] distribution produces the mean 〈[Fe/H]〉=−0.01 and dispersion σ[Fe/H]= 0.08. The near-solar metallicity and small dispersion of σ[Fe/H] of clump stars of the Galaxy obtained in this paper confirm the theoretical model of the Hipparcos clump by Girardi & Salaris. This suggests that nearby clump stars are (in the mean) relatively young objects, reflecting mainly the near-solar metallicities developed in the local disc during the last few Gyr of its history. We find iron-group element to iron-abundance ratios in clump giants to be close to solar.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations21
Published2010
Admission routes1
Has abstractyes

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