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Record W2089676823 · doi:10.1088/2041-8205/792/2/l36

CONSTRAINING THE EVOLUTIONARY FATE OF CENTRAL COMPACT OBJECTS: “OLD” RADIO PULSARS IN SUPERNOVA REMNANTS

2014· article· en· W2089676823 on OpenAlexaff
Slavko Bogdanov, C.‐Y. Ng, V. M. Kaspi

Bibliographic record

VenueThe Astrophysical Journal Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPulsarPhysicsAstrophysicsNeutron starSupernovaAstronomyPopulationMillisecond pulsarMagnetic dipoleStarsDipole

Abstract

fetched live from OpenAlex

Central compact objects (CCOs) constitute a population of radio-quiet, slowly spinning (⩾100 ms) young neutron stars with anomalously high thermal X-ray luminosities. Their spin-down properties imply weak dipole magnetic fields (∼10 10–11 G) and characteristic ages much greater than the ages of their host supernova remnants (SNRs). However, CCOs may posses strong "hidden" internal magnetic fields that may re-emerge on timescales of ≳10 kyr, with the neutron star possibly activating as a radio pulsar in the process. This suggests that the immediate descendants of CCOs may be masquerading as slowly spinning "old" radio pulsars. We present an X-ray survey of all ordinary radio pulsars within 6 kpc that are positionally coincident with Galactic SNRs in order to test the possible connection between the supposedly old but possibly very young pulsars and the SNRs. None of the targets exhibit anomalously high thermal X-ray luminosities, suggesting that they are genuine old ordinary pulsars unrelated to the superposed SNRs. This implies that CCOs are either latent radio pulsars that activate long after their SNRs dissipate or they remain permanently radio-quiet. The true descendants of CCOs remain at large.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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
Published2014
Admission routes1
Has abstractyes

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