Population nucleosynthesis in single and binary stars
Bibliographic record
Abstract
We present a synthetic algorithm to rapidly calculate nucleosynthetic yields from populations of single and binary stars for use in population synthesis, globular cluster and Galactic chemical evolution simulations. Single star nucleosynthesis is fitted directly to full evolution models and our model includes first, second and third dredge-ups with s-process enhancements, an analytic calculation for hot-bottom burning of CNO, NeNa and MgAl isotopes, surface enhancements due to wind loss in massive stars and core-collapse supernova yields. Even though this algorithm operates about 107 times faster than full evolution and nucleosynthesis calculations, agreement with such models is good. We extend the single star model to include prescriptions of binary star interactions, notably mass loss and gain by stellar winds and Roche-lobe overflow, novae and type Ia supernovae. As examples of the application of our algorithm we present models of some interesting systems containing chemically peculiar stars that may occur in binaries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".