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Record W2763306320 · doi:10.1103/physrevd.97.124039

Global simulations of strongly magnetized remnant massive neutron stars formed in binary neutron star mergers

2018· article· en· W2763306320 on OpenAlexfundno aff
Kenta Kiuchi, Koutarou Kyutoku, Yuichiro Sekiguchi, Masaru Shibata

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersAgenzia Italiana per la Cooperazione allo SviluppoKyoto UniversityRIKENNational Astronomical Observatory of JapanUniversity of TokyoJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyCanadian Foundation for Climate and Atmospheric SciencesInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsNeutron starAstrophysicsPhysicsX-ray binaryBinary numberStar (game theory)AstronomyMathematics

Abstract

fetched live from OpenAlex

This article reports on the results of novel, extremely high-resolution simulations of the general relativistic magnetohydrodynamics (MHD) of neutron star mergers, focussing on angular momentum transport due to the MHD turbulence. The authors show that the Kelvin-Helmholtz instability at merger amplifies the magnetic energy to $\ensuremath{\sim}1%$ of the thermal energy.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.448
Teacher spread0.430 · 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

Citations236
Published2018
Admission routes1
Has abstractyes

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