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Record W2047340280 · doi:10.1086/587160

Additional Ultracool White Dwarfs Found in the Sloan Digital Sky Survey

2008· article· en· W2047340280 on OpenAlexaff
Hugh C. Harris, Evalyn Gates, G. Gyuk, Mark SubbaRao, Scott F. Anderson, Patrick B. Hall, Jeffrey A. Munn, James Liebert, G. R. Knapp, Dmitry Bizyaev, Elena Malanushenko, V. P. Malanushenko, Kaike Pan, Donald P. Schneider, J. A. Smith

Bibliographic record

VenueThe Astrophysical Journal · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsYork University
Fundersnot available
KeywordsWhite dwarfBalmer seriesPhysicsAstrophysicsSkyPhotometry (optics)StarsAstronomyStellar classificationSpectral lineProper motionEmission spectrum

Abstract

fetched live from OpenAlex

We identify seven new ultracool white dwarfs discovered in the Sloan Digital Sky Survey (SDSS). The SDSS photometry, spectra, and proper motions are presented, and additional BVRI data are given for these and other previously discovered ultracool white dwarfs. The observed colors span a remarkably wide range, qualitatively similar to colors predicted by models for very cool white dwarfs. One of the new stars (SDSS J1251+44) exhibits strong collision-induced absorption (CIA) in its spectra, while the spectra and colors of the other six are consistent with mild CIA. Another of the new discoveries (SDSS J2239+00A) is part of a binary system—its companion is also a cool white dwarf, and other data indicate that the companion exhibits an infrared flux deficiency, making this the first binary system composed of two CIA white dwarfs. A third discovery (SDSS J0310–00) has weak Balmer emission lines. The proper motions of all seven stars are consistent with membership in the disk or thick disk.

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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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

Citations34
Published2008
Admission routes1
Has abstractyes

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