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Record W2002971762 · doi:10.1088/0004-6256/142/6/193

THE RAVE CATALOG OF STELLAR ELEMENTAL ABUNDANCES: FIRST DATA RELEASE

2011· article· en· W2002971762 on OpenAlexaff
C. Boeche, A. Siebert, M. Williams, Roelof S. de Jong, Matthias Steinmetz, J. P. Fulbright, G. Ruchti, O. Bienaymé, Joss Bland‐Hawthorn, R. Campbell, K. C. Freeman, B. K. Gibson, G. Gilmore, E. K. Grebel, A. Helmi, U. Munari, Julio F. Navarro, Q. A. Parker, W. Reid, G. M. Seabroke, A. Siviero, F. G. Watson, Rosemary F. Ġ. Wyse, T. Zwitter

Bibliographic record

VenueThe Astronomical Journal · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsSpectral lineGalaxyAstrophysicsPhysicsLine (geometry)StarsMilky WayAnalytical Chemistry (journal)ChemistryAstronomyMathematicsGeometry

Abstract

fetched live from OpenAlex

We present chemical elemental abundances for 36,561 stars observed by the RAdial Velocity Experiment (RAVE), an ambitious spectroscopic survey of our Galaxy at Galactic latitudes |b| > 25° and with magnitudes in the range 9<I DENIS <13. RAVE spectra cover the Ca-triplet region at 8410-8795 Å with resolving power R ∼ 7500. This first data release of the RAVE chemical catalog is complementary to the third RAVE data release of radial velocities and stellar parameters, and it contains chemical abundances for the elements Mg, Al, Si, Ca, Ti, Fe, and Ni, with a mean error of ∼0.2dex, as judged from accuracy tests performed on synthetic and real spectra. Abundances are estimated through a dedicated processing pipeline in which the curve of growth of individual lines is obtained from a library of absorption line equivalent widths to construct a model spectrum that is then matched to the observed spectrum via a χ2 minimization technique. We plan to extend this pipeline to include estimates for other elements, such as oxygen and sulfur, in future data releases. © 2011 The American Astronomical Society. All rights reserved.

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 score0.029
Threshold uncertainty score0.529

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.237
Teacher spread0.201 · 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

Citations77
Published2011
Admission routes1
Has abstractyes

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