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Record W2172161529 · doi:10.1051/0004-6361/201219607

The relation between chemical abundances and kinematics of the Galactic disc with RAVE

2013· article· en· W2172161529 on OpenAlexafffund
C. Boeche, C. Chiappini, Ivan Minchev, Matthew D. Williams, Matthias Steinmetz, Sanjib Sharma, G. Kordopatis, Joss Bland‐Hawthorn, O. Bienaymé, B. K. Gibson, G. Gilmore, E. K. Grebel, A. Helmi, U. Munari, Julio F. Navarro, Q. A. Parker, W. Reid, G. M. Seabroke, A. Siebert, A. Siviero, F. G. Watson, Rosemary F. Ġ. Wyse, T. Zwitter

Bibliographic record

VenueAstronomy and Astrophysics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
FundersLeibniz-GemeinschaftAustralian Research CouncilScience and Technology Facilities CouncilAustralian Astronomical Optics-MacquarieDeutsche ForschungsgemeinschaftJavna Agencija za Raziskovalno Dejavnost RSMacquarie UniversityNatural Sciences and Engineering Research Council of CanadaJohns Hopkins UniversityW. M. Keck FoundationAgence Nationale de la RechercheAustralian National UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungIstituto Nazionale di AstrofisicaNational Science Foundation
KeywordsAstrophysicsStarsGalactic planePhysicsSubdwarfEccentricity (behavior)AstronomyStellar kinematicsKinematicsRadial velocityMilky WayWhite dwarf

Abstract

fetched live from OpenAlex

Aims. We study the relations between stellar kinematics and chemical abundances of a large sample of RAVE giants in search of the selection criteria needed for disentangling different Galactic stellar populations, such as thin disc, thick disc and halo. A direct comparison between the chemo-kinematic relations obtained with our medium spectroscopic resolution data and those obtained from a high-resolution sample is carried out with the aim of testing the robustness of the RAVE data.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations58
Published2013
Admission routes2
Has abstractyes

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