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Record W2574018563 · doi:10.1051/0004-6361/201630012

A RAVE investigation on Galactic open clusters

2017· article· en· W2574018563 on OpenAlexafffund
Claudia Conrad, R.‐D. Scholz, N. V. Kharchenko, А. Э. Пискунов, S. Röser, E. Schilbach, Roelof S. de Jong, O. Schnurr, Matthias Steinmetz, E. K. Grebel, T. Zwitter, O. Bienaymé, Joss Bland‐Hawthorn, B. K. Gibson, G. Gilmore, G. Kordopatis, Andrea Kunder, Julio F. Navarro, Q. A. Parker, W. Reid, G. M. Seabroke, A. Siviero, Fred Watson, Rosemary F. Ġ. Wyse

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
FundersLeibniz-GemeinschaftAustralian Research CouncilScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaAustralian Astronomical Optics-MacquarieDeutsche ForschungsgemeinschaftJavna Agencija za Raziskovalno Dejavnost RSMacquarie UniversityJohns Hopkins UniversityRussian Foundation for Basic ResearchW. M. Keck FoundationAgence Nationale de la RechercheLeibniz-Institut für Astrophysik PotsdamAustralian National UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungIstituto Nazionale di AstrofisicaNational Science Foundation
KeywordsPhysicsOpen clusterAstrophysicsStar clusterStellar populationStarsCluster (spacecraft)Context (archaeology)AstronomyStar formationPopulationStar (game theory)MedicineGeography

Abstract

fetched live from OpenAlex

Context. It is generally agreed upon that stars form in open clusters (OCs) and stellar associations, but little is known about structures in the Galactic OC population; whether OCs and stellar associations are born isolated or if they prefer to form in groups, for example. Answering this question provides new insight into star and cluster formation, along with a better understanding of Galactic structures.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.243
Teacher spread0.218 · 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
Published2017
Admission routes2
Has abstractyes

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