MétaCan
Menu
Back to cohort
Record W3193959975 · doi:10.3847/2041-8213/ac1f99

Cooling Delays from Iron Sedimentation and Iron Inner Cores in White Dwarfs

2021· article· en· W3193959975 on OpenAlexafffund
M. E. Caplan, Ian F. Freeman, C. J. Horowitz, A. Cumming, Earl P. Bellinger

Bibliographic record

VenueThe Astrophysical Journal Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMcGill University
FundersOffice of ScienceDanmarks GrundforskningsfondEli Lilly and CompanyNational Research FoundationLilly EndowmentNatural Sciences and Engineering Research Council of CanadaStellar Astrophysics CentreU.S. Department of Energy
KeywordsWhite dwarfAstrophysicsPhysicsCrystalliteInner coreAsteroseismologyMesoscopic physicsStarsChemistryCrystallographyGeophysicsCondensed matter physics

Abstract

fetched live from OpenAlex

Abstract Do white dwarfs have inner cores made of iron? Neutron-rich nuclei like 56Fe experience a net gravitational force and sediment toward the core. Using new phase diagrams and molecular dynamics simulations, we show that 56Fe should separate into mesoscopic Fe-rich crystallites due to its large charge relative to the background. At solar abundances, these crystallites rapidly precipitate and form an inner core of order 100 km and 10−3 M ⊙ that may be detectable with asteroseismology. Associated cooling delays could be up to a Gyr for low-mass white dwarfs but are only ∼0.1 Gyr for massive white dwarfs, so while this mechanism may contribute to the Q-branch the heating is insufficient to fully explain it.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations22
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe Astrophysical Journal LettersSame topicStellar, planetary, and galactic studiesFrench-language works237,207