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THE NATURE OF THE TYPE C SEMI-REGULAR VARIABLES

2017· article· en· W61630715 on OpenAlexaff
D. G. Turner

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsSupergiantAstrophysicsLuminosityPhysicsSupernovaBrightnessCosmic distance ladderStarsMilky WayRed supergiantAstronomyVariable starGalaxy

Abstract

fetched live from OpenAlex

The type C semi-regular (SRC) variables are M-type supergiants like Betelgeuse that undergo regular periodic brightness fluctuations superposed upon seemingly irregular trends. Many of the 55 recognized Milky Way M-type supergiants belonging to the class are multi-periodic, with radial pulsation almost certainly the source of their primary short-period luminosity changes. Noted here are additional characteristics of the stars, not supplied in the GCVS, that can be recognized in their growing archival inventories: amplitude for both long-term and short-term light variability increasing with luminosity, regular changes in mean brightness possibly tied to evolution, and a period-luminosity relation in need of further observational corroboration. As the immediate stellar precursors to many Type II supernovae and as auxiliary standard candles for the extragalactic distance scale, SRC variables are in need of more extensive observational studies to elucidate their many intriguing properties.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.099
GPT teacher head0.465
Teacher spread0.367 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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