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Record W2025806659 · doi:10.1088/0004-6256/135/3/892

ROTATION AND MACROTURBULENCE IN METAL-POOR FIELD RED GIANT AND RED HORIZONTAL BRANCH STARS

2008· article· en· W2025806659 on OpenAlexaff
Bruce W. Carney, David F. Gray, David Yong, David W. Latham, N. Manset, Rachel Zelman, John B. Laird

Bibliographic record

VenueThe Astronomical Journal · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsWestern University
Fundersnot available
KeywordsStarsPhysicsAstrophysicsHorizontal branchRed-giant branchSubgiantK-type main-sequence starRotation (mathematics)AstronomyMetallicityT Tauri starGlobular clusterGeometryMathematics

Abstract

fetched live from OpenAlex

We report the results for rotational velocities, Vrot sin i, and macroturbulence dispersions, ζRT, for 12 metal-poor field red giant branch (RGB) stars and 7 metal-poor field red horizontal branch (RHB) stars. The results are based on Fourier transform analyses of absorption line profiles from high-resolution (R ≈ 120, 000), high-S/N ( ≈ 215 per pixel; ≈ 345 per resolution element) spectra obtained with the Gecko spectrograph at CFHT. The stars were selected from the studies of 20 RHB and 116 RGB stars from Carney et al. (2003, 2007), based primarily on larger-than-average line broadening values. We find that ζRT values for the metal-poor RGB stars are very similar to those for metal-rich disk giants studied earlier by Gray and his collaborators. Six of the RGB stars have small rotational values, less than 2.0 km s −1, while five show significant rotation/enhanced line broadening, over 3 km s −1. We confirm the rapid rotation rate for RHB star HD 195636, found earlier by Preston (1997). This star’s rotation is comparable to that of the fastest known rotating

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.348

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)0.0000.000

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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.

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

Citations70
Published2008
Admission routes1
Has abstractyes

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