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Record W4389455195 · doi:10.1093/mnras/stad3773

The 40 pc sample of white dwarfs from <i>Gaia</i>

2023· article· en· W4389455195 on OpenAlexfundno aff
Mairi W O’Brien, B. Klein, D. Koester, Carl Melis, Antoine Bédard, Elena Cukanovaite, Tim Cunningham, Alexandra E. Doyle, B. T. Gänsicke, N. P. Gentile Fusillo, Mark Hollands, J McCleery, Ingrid Pelisoli, Silvia Toonen, Alycia J. Weinberger, B. Zuckerman

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilEuropean Southern ObservatoryNatural Sciences and Engineering Research Council of CanadaCalifornia Institute of TechnologyEuropean CommissionUniversity of WarwickEuropean Research CouncilLeverhulme TrustNational Science FoundationEuropean Space AgencyNational Aeronautics and Space AdministrationW. M. Keck FoundationUniversity of California
KeywordsWhite dwarfPhysicsAstrophysicsPhotometry (optics)Massive compact halo objectAstrometryAstronomyBrown dwarfStars

Abstract

fetched live from OpenAlex

ABSTRACT We present a comprehensive overview of a volume-complete sample of white dwarfs located within 40 pc of the Sun, a significant proportion of which were detected in Gaia Data Release 3 (DR3). Our DR3 sample contains 1076 spectroscopically confirmed white dwarfs, with just five candidates within the volume remaining unconfirmed (> 99 per cent spectroscopic completeness). Additionally, 28 white dwarfs were not in our initial selection from Gaia DR3, most of which are in unresolved binaries. We use Gaia DR3 photometry and astrometry to determine a uniform set of white dwarf parameters, including mass, effective temperature, and cooling age. We assess the demographics of the 40 pc sample, specifically magnetic fields, binarity, space density, and mass distributions.

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations81
Published2023
Admission routes1
Has abstractyes

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