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Record W6910468261 · doi:10.4225/08/59cfb37bd1b5a

Parkes observations for project P885 semester 2015OCTS

2015· dataset· en· W6910468261 on OpenAlexaff

Bibliographic record

VenueCSIRO · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsMcGill University
Fundersnot available
KeywordsMagnetarTrack (disk drive)Neutron starRadio waveRadio communicationsFlux (metallurgy)Variable (mathematics)

Abstract

fetched live from OpenAlex

We propose to observe 1E 1547.0-5408 weekly, one of two magnetars known to currently emit radio pulsations, to track its rotation, flux density, and polarisation. Less frequently we will monitor PSR J1622-4950 and XTE J1810-197, which used to emit radio pulsations, to be on the lookout for their revival. After years with unsteady radio emission, 1E 1547.0-5408 is now detected 100% of the time, allowing us to obtain a continuous record of its torque which illuminates the continued release of magnetic energy in the neutron star. Our broader aims are to develop a better understanding of the dynamical behaviour of magnetar magnetospheres, and to establish the conditions under which radio emission takes place therein, in addition to characterising their variable radio emission in order to place constraints on the emission mechanisms, which in at least some respects differ from those of ordinary 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.052
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0490.094

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 head0.291
GPT teacher head0.393
Teacher spread0.103 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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