NUGRID STELLAR DATA SET. I. STELLAR YIELDS FROM H TO BI FOR STARS WITH METALLICITIES Z = 0.02 and Z = 0.01
Bibliographic record
Abstract
ABSTRACT We provide a set of stellar evolution and nucleosynthesis calculations that applies established physics assumptions simultaneously to low- and intermediate-mass and massive star models. Our goal is to provide an internally consistent and comprehensive nuclear production and yield database for applications in areas such as presolar grain studies. Our non-rotating models assume convective boundary mixing (CBM) where it has been adopted before. We include 8 (12) initial masses for Z = 0.01 (0.02). Models are followed either until the end of the asymptotic giant branch phase or the end of Si burning, complemented by simple analytic core-collapse supernova (SN) models with two options for fallback and shock velocities. The explosions show which pre-SN yields will most strongly be effected by the explosive nucleosynthesis. We discuss how these two explosion parameters impact the light elements and the s and p process. For low- and intermediate-mass models, our stellar yields from H to Bi include the effect of CBM at the He-intershell boundaries and the stellar evolution feedback of the mixing process that produces the pocket. All post-processing nucleosynthesis calculations use the same nuclear reaction rate network and nuclear physics input. We provide a discussion of the nuclear production across the entire mass range organized by element group. The entirety of our stellar nucleosynthesis profile and time evolution output are available electronically, and tools to explore the data on the NuGrid VOspace hosted by the Canadian Astronomical Data Centre are introduced.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".