Reddenings of FGK supergiants and classical Cepheids from spectroscopic data
Bibliographic record
Abstract
Accurate and homogeneous atmospheric parameters (Teff, log g, Vt, [Fe/H]) are derived for 74 FGK non-variable supergiants from high-resolution, high signal-to-noise ratio, echelle spectra. Extremely high precision for the inferred effective temperatures (10–40 K) is achieved by using the line-depth ratio method. The new data are combined with atmospheric values for 164 classical Cepheids, observed at 675 different pulsation phases, taken from our previously published studies. The derived values are correlated with unreddened B−V colours compiled from the literature for the investigated stars in order to obtain an empirical relationship of the form (B−V)0= 57.984 − 10.3587(log Teff)2+ 1.675 72(log Teff)3− 3.356 log g+ 0.0321Vt+ 0.2615[Fe/H]+ 0.8833(log g)(log Teff). The expression is used to estimate colour excesses E(B−V) for individual supergiants and classical Cepheids, with a precision of ±0.05 mag for supergiants and Cepheids with n= 1–2 spectra, reaching ±0.025 mag for Cepheids with n > 2 spectra, matching uncertainties for the most sophisticated photometric techniques. The reddening scale is also a close match to the system of space reddenings for Cepheids. The application range is for spectral types F0–K0 and luminosity classes I and II.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".