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Record W3025256207

Cool White Dwarfs Selection with Pan-STARRS Proper Motions

2015· article· en· W3025256207 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLiverpool John Moores University · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersPlanetary Science DivisionScience Mission DirectorateSmithsonian Astrophysical ObservatoryMax-Planck-Institut für AstronomieQueen's UniversityJohns Hopkins UniversityQueen's University BelfastNational Aeronautics and Space AdministrationEötvös Loránd TudományegyetemNational Central UniversitySpace Telescope Science InstituteDurham UniversitySmithsonian InstitutionNational Science Foundation
KeywordsProper motionWhite dwarfOutlierPopulationLimit (mathematics)Volume (thermodynamics)PhysicsGeodesyComputer scienceAstrophysicsGeologyArtificial intelligenceMathematicsStarsMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

The use of Reduced Proper Motion in identifying isolated white dwarfs has long been used as a proxy for the absolute magnitude in a population with known kinematics. This, however, introduces a proper motion detection limit on top of the existing photometric limit. How the survey volume is hampered by this extra parameter is discussed in Hambly et al. (2012). In this work, we discuss some robust outlier rejection methods in order to minimise the proper motion limit and hence maximise the survey volume. The generalised volume, corrected for the distance of the Sun from the Galactic Plane, is integrated explicitly.

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.

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 score0.212
Threshold uncertainty score0.630

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 head0.012
GPT teacher head0.185
Teacher spread0.173 · 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