Line‐Depth Ratios: Temperature Indices for Giant Stars
Bibliographic record
Abstract
Ratios of the depths of appropriately chosen spectral lines are shown to be excellent indicators of stellar temperatures for giant stars in the G3 to K3 spectral type range. We calibrate five line‐depth ratios against B−V and R−I color indices and then translate these into temperatures. Our goal is to set up line‐depth ratios to (1) accurately monitor any temperature variations of a few degrees or less that may occur during magnetic cycles or oscillations and (2) rank giants precisely on a temperature coordinate. This is not an absolute calibration of stellar temperatures. We show how giant spectra can be misleading because of the complex dependences of spectral lines on metallicity and absolute magnitude as well as temperature, and it is essential to make corrections to accommodate these complications. The five line‐depth ratios we use yield precision for monitoring, i.e., detecting temperature variations, of 4 K from a single exposure. Ranking giants by temperature can be done with errors of ∼25 K but could be improved with better determinations of the metallicity and absolute‐magnitude corrections.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
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".