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Record W2078234866 · doi:10.1051/0004-6361:20053392

Radio spectral index study of the SNRs OA184 and VRO42.05.01

2005· article· en· W2078234866 on OpenAlexafffundabout

Bibliographic record

VenueAstronomy and Astrophysics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSpectral indexFlux (metallurgy)Supernova remnantGalactic planeSpectral lineSpectral resolutionSupernovaRidge

Abstract

fetched live from OpenAlex

New images of the Supernova Remnants (SNRs) OA184 and VRO42.05.01 are presented at 408 MHz and 1420 MHz, from the Canadian Galactic Plane Survey (CGPS) data. The SNRs' flux densities at both 408 MHz and 1420 MHz are found and corrected for flux densities from compact sources within the SNRs. The integrated flux density based spectral indices (S) are 0.25 ± 0.03 for OA184 and 0.36 ± 0.06 for VRO42.05.01. These agree with the respective plot spectral indexes of 0.23 ± 0.06 and 0.36 ± 0.03. OA184's spectral index is smaller than previously published values. The older flux density values of OA184 from lower resolution data include contributions from a non-SNR ridge emission region and from compact sources within OA184. Subtracting these contributions results in a spectral index of 0.32 ± 0.06 (38 MHz to 2695 MHz) or 0.28 ± 0.06 (408 MHz to 2695 MHz). Correction of published flux densities for compact sources for VRO.42.05.01 results in a spectral index of 0.32 ± 0.05 for 38 MHz to 2695 MHz. We also find spatial variations of spectral index. For OA184 α varies from 0.1 to 0.3 (with errors 0.1). For VRO42.05.01, the shell region has and the wing region has (with errors 0.03).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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 designObservational
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

Citations28
Published2005
Admission routes3
Has abstractyes

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