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Record W3129766788 · doi:10.26180/13611644.v1

On the Nature of Neutron Stars in Accreting Systems

2021· dissertation· en· W3129766788 on OpenAlexfundno aff
A J Goodwin

Bibliographic record

VenueFigshare · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Research FoundationNatural Sciences and Engineering Research Council of CanadaEuropean CommissionScience and Technology Commission of Shanghai MunicipalityAustralian GovernmentNew York University Abu DhabiNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsNeutron starPhysicsAstrophysicsStarsAstronomy

Abstract

fetched live from OpenAlex

Neutron stars, the dense remnants of dead stars, are unique laboratories of fundamental physics. Isolated neutron stars are hard to find, but neutron stars with binary companions may emit X-rays due to the supply of gas from the other star. This high-energy radiation enables us to detect them with telescopes. Such “accreting” neutron stars give us direct information from the extremes of physics: from the densest matter known, through nuclear reactions in energetic environments, to how material moves in strong magnetic and gravitational fields. This research aims to further understand accreting neutron star systems by using computer models and observations.

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)0.003
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: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.336
Teacher spread0.318 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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