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Record W2045462231 · doi:10.1086/301292

Fundamental Parameters of Low-Mass Stars from Broadband Photometry. I. Method and First Results

2000· article· en· W2045462231 on OpenAlexaff
C. A. P. Krawchuk, Peter Dawson, M. M. De Robertis

Bibliographic record

VenueThe Astronomical Journal · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsTrent UniversityDominion Astrophysical ObservatoryHerzberg Institute of AstrophysicsYork University
Fundersnot available
KeywordsPhysicsPhotometry (optics)StarsAstrophysicsMetallicityEffective temperatureAstronomyStellar classification

Abstract

fetched live from OpenAlex

Spectral energy distributions from recent model atmospheres covering a broad range of temperature and metallicity are used to synthesize colors for low-mass stars. Color-color diagrams can therefore be prepared in which each point corresponds to a particular combination of T eff and M/H. Comparison of observed with synthetic colors yields estimates of those parameters generally, but not necessarily under the assumption that log g cgs = 5. Results are presented for twelve stars of astrophysical interest.

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)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.239
Teacher spread0.227 · 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
GenreMethods

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

Citations11
Published2000
Admission routes1
Has abstractyes

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