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Record W1985809928 · doi:10.1086/345493

Radio Emission from Magnetars

2002· article· en· W1985809928 on OpenAlexaff
Maxim Lyutikov

Bibliographic record

VenueThe Astrophysical Journal · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMagnetarPhysicsAstrophysicsNarrowbandBurstingRadio spectrumSolar flareDissipationAstronomyRadio waveRadio frequencyNeutron starOpticsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We discuss properties of the expected radio emission from soft gamma-ray repeaters (SGRs) during their bursting activity in the framework of the model of Thompson, Lyutikov, & Kulkarni, in which the high-energy emission is powered by the dissipation of superstrong magnetic fields in the magnetospheres through reconnection-type events. Drawing on analogies with solar flares, we predict that coherent radio emission resembling solar type III radio bursts may be emitted in SGRs during X-ray bursts. The radio emission should have correlated pulse profiles with X-rays, a narrowband-type radio spectrum with Δν ≤ ν, with the typical frequency ν ≥ 10 GHz, and, possibly, a drifting central frequency. We encourage sensitive radio observations of SGRs during the bursting activity.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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 designTheoretical or conceptual
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

Citations81
Published2002
Admission routes1
Has abstractyes

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