ISOCHRONES FOR OLD (>5 GYR) STARS AND STELLAR POPULATIONS. I. MODELS FOR –2.4 ⩽ [Fe/H] ⩽+0.6, 0.25 ⩽<i>Y</i>⩽ 0.33, AND –0.4 ⩽ [α/Fe] ⩽+0.4
Bibliographic record
Abstract
Canonical grids of stellar evolutionary sequences have been computed for the helium mass-fraction abundances Y = 0.25, 0.29, and 0.33, and for iron abundances that vary from −2.4 to +0.4 (in 0.2 dex increments) when [α/Fe] =+0.4, or for the ranges −2.0 ⩽ [Fe/H] ⩽+0.6, −1.8 ⩽ [Fe/H] ⩽+0.6 when [α/Fe] =0.0 and −0.4, respectively. The grids, which consist of tracks for masses from to 1.1– (depending on the metallicity) are based on up-to-date physics, including the gravitational settling of helium (but not metals diffusion). Interpolation software is provided to generate isochrones for arbitrary ages between ≈5 and 15 Gyr and any values of Y , [α/Fe], and [Fe/H] within the aformentioned ranges. Comparisons of isochrones with published color–magnitude diagrams (CMDs) for the open clusters M67 ([Fe/H] ≈0.0) and NGC 6791 ([Fe/H] ≈0.3) and for four of the metal-poor globular clusters (47 Tuc, M3, M5, and M92) indicate that the models for the observed metallicities do a reasonably good job of reproducing the locations and slopes of the cluster main sequences and giant branches. The same conclusion is reached from a consideration of plots of nearby subdwarfs that have accurate Hipparcos parallaxes and metallicities in the range −2.0 ≲ [Fe/H] ≲ −1.0 on various CMDs and on the (log T eff , M V ) diagram. A relatively hot temperature scale similar to that derived in recent calibrations of the infrared flux method is favored by both the isochrones and the adopted color transformations, which are based on the latest MARCS model atmospheres.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".