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Record W4362668827 · doi:10.3847/1538-4365/acc1de

A Statistical Analysis of Galactic Radio Supernova Remnants

2023· article· en· W4362668827 on OpenAlexaff
S. Ranasinghe, D. A. Leahy

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysicsAstrophysicsSpectral indexSupernovaStandard deviationGalactic planeSurface brightnessType (biology)GalaxyAstronomyStatisticsSpectral lineMathematics

Abstract

fetched live from OpenAlex

Abstract We present a revised table of 390 Galactic radio supernova remnants (SNRs) and their basic parameters. Statistical analyses are performed on SNR diameters, ages, spectral indices, Galactic heights, and spherical symmetries. Furthermore, the accuracy of distances estimated using the Σ–D relation is examined. The arithmetic mean of the Galactic SNR diameters is 30.5 pc with standard error 1.7 pc and standard deviation 25.4 pc. The geometric mean and geometric standard deviation factor of Galactic SNR diameters is 21.9 pc and 2.4, respectively. We estimate ages of 97 SNRs and find a supernova (SN) birth rate lower than, but within 2 σ of, currently accepted values for the SN birth rate. The mean spectral index of shell-type SNRs is −0.51 ± 0.01 and no correlations are found between spectral indices and the SNR parameters of molecular cloud association, SN type, diameter, Galactic height, and surface brightness. The Galactic height distribution of SNRs is best described by an exponential distribution with a scale height of 48 ± 4 pc. The spherical symmetry measured by the ovality of radio SNRs is not correlated to any other SNR parameters considered here or to explosion type.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.596
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.258
Teacher spread0.246 · 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 teacher head, not a consensus.

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

Citations33
Published2023
Admission routes1
Has abstractyes

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