Mass and metallicity requirement in stellar models for galactic chemical evolution applications
Bibliographic record
Abstract
We used a one-zone chemical evolution model to address the question of how many masses and metallicities are required in grids of massive stellar models in order to ensure reliable galactic chemical evolution predictions. We used a set of yields that includes seven masses between 13 and 30 M⊙, 15 metallicities between 0 and 0.03 in mass fraction, and two different remnant mass prescriptions. We ran several simulations where we sampled subsets of stellar models to explore the impact of different grid resolutions. Stellar yields from low- and intermediate-mass stars and from Type Ia supernovae have been included in our simulations, but with a fixed grid resolution. We compared our results with the stellar abundances observed in the Milky Way for O, Na, Mg, Si, Ca, Ti, and Mn. Our results suggest that the range of metallicity considered is more important than the number of metallicities within that range, which only affects our numerical predictions by about 0.1 dex. We found that our predictions at [Fe/H] ≲ −2 are very sensitive to the metallicity range and the mass sampling used for the lowest metallicity included in the set of yields. Variations between results can be as high as 0.8 dex. At higher [Fe/H], we found that the required number of masses depends on the element of interest and on the remnant mass prescription. With a monotonic remnant mass prescription where every model explodes as a core-collapse supernova, the mass resolution induces variations of 0.2 dex on average. But with a remnant mass prescription that includes islands of non-explodability, the mass resolution can cause variations of about 0.2–0.7 dex depending on the choice of the lower limit of the metallicity range. With such a remnant mass prescription, explosive or non-explosive models can be missed if not enough masses are selected, resulting in over- or underestimations of the mass ejected by massive stars.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".