Chemical separation of stellar populations: analytic solutions for chemical evolution models with metallicity-dependent yields
Bibliographic record
Abstract
ABSTRACT Stellar abundances of elements with production channels that are metallicity-dependent (most notably aluminium) have provided an empirical route for separating different Galactic components. We present ‘single-zone’ analytic solutions for the chemical evolution of galaxies when the stellar yields are metallicity-dependent. Our solutions assume a constant star formation efficiency, a constant mass-loading factor and that the yields are linearly dependent on the interstellar medium abundance (with the option of a saturation of the yields at high metallicity). We demonstrate how the metallicity dependence of the yields can be mathematically considered as a system-dependent delay time (approximately equal to the system’s depletion time) that, when combined with system-independent delay times arising from stellar evolutionary channels, produces the separation of different systems based on their star formation efficiency and mass-loading factor. The utility of the models is highlighted through comparisons with data from the APOGEE spectroscopic survey. We provide a comprehensive discussion of the chemical evolution models in the [Al/Fe]–[Mg/Fe] plane, a diagnostic plane for the separation of in situ and accreted Galactic components. Extensions of the models are presented, allowing for the modelling of more complex behaviours largely through the linear combination of the presented simpler solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".