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Record W4322584719 · doi:10.1017/s1743921322001016

Heating from Electron Captures by Nuclei in Magnetar Crusts

2020· article· en· W4322584719 on OpenAlexaboutno aff
N. Chamel, A. F. Fantina, L. Suleiman, J. L. Zdunik, P. Haensel

Bibliographic record

VenueProceedings of the International Astronomical Union · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetarPhysicsElectronMagnetic fieldNeutron starAstrophysicsPulsarLandau quantizationLuminosityAtomic physicsNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract The decay of the magnetic field in the interior of a magnetar may trigger electron captures by nuclei in the stellar crust, thus providing an internal source of heating. In turn, the onset of electron captures and the heat released are altered by the magnetic field due to the Landau–Rabi quantization of electron motion. The loss of magnetic pressure might also lead to pycnonuclear fusions of the lightest elements. The maximum amount of heat that can be possibly released by each reaction and their location are calculated using nuclear data from both experiments and theoretical predictions of the Brussels-Montreal models based on self-consistent Hartree-Fock-Bogoliubov calculations. Results are found to be consistent with those inferred empirically by comparing neutron-star cooling simulations with observed thermal luminosity of soft gamma-ray repeaters and anomalous X-ray pulsars.

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 score0.009

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.256
Teacher spread0.249 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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