SYGMA: Stellar Yields for Galactic Modeling Applications
Bibliographic record
Abstract
Abstract The Stellar Yields for Galactic Modeling Applications (SYGMA) code is an open-source module that models the chemical ejecta and feedback of simple stellar populations (SSPs). It is intended for use in hydrodynamical simulations and semi-analytic models of galactic chemical evolution. The module includes the enrichment from asymptotic giant branch (AGB) stars, massive stars, Type Ia supernovae (SNe Ia), and compact binary mergers. An extensive and extendable stellar yields library includes the NuGrid yields with all elements and many isotopes up to Bi. Stellar feedback from mechanic and frequency-dependent radiative luminosities are computed based on NuGrid stellar models and their synthetic spectra. The module further allows for customizable initial mass functions and SN Ia delay-time distributions to calculate time-dependent ejecta based on stellar yield input. A variety of r-process sites can be included. A comparison of SSP ejecta based on NuGrid yields with those from Portinari et al. and Marigo reveals up to factors of 3.5 and 4.8 less C and N enrichment from AGB stars at low metallicity, a result we attribute to NuGrid’s modeling of hot-bottom burning. Different core-collapse supernova explosions and fallback prescriptions may lead to substantial variations for the accumulated ejecta of C, O and Si in the first 107 years at Z = 0.001. An online interface of the open-source SYGMA module enables interactive simulations, analysis, and data extraction of the evolution of all species formed by the evolution of simple stellar populations.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".