Chemical evolution of fluorine in the Milky Way
Bibliographic record
Abstract
ABSTRACT Fluorine has many different potential sites and channels of production, making narrowing down a dominant site of fluorine production particularly challenging. In this work, we investigate which sources are the dominant contributors to the galactic fluorine by comparing chemical evolution models to observations of fluorine abundances in Milky Way stars covering a metallicity range of −2 < [Fe/H] < 0.4 and upper limits in the range of −3.4 < [Fe/H] < −2.3. In our models, we use a variety of stellar yield sets in order to explore the impact of varying both asymptotic giant branch (AGB) and massive star yields on the chemical evolution of fluorine. In particular, we investigate different prescriptions for initial rotational velocity in massive stars as well as a metallicity-dependent mix of rotational velocities. We find that the observed [F/O] and [F/Fe] abundance ratios at low metallicity and the increasing trend of [F/Ba] at [Fe/H] ≳ −1 can only be reproduced by chemical evolution models assuming, at all metallicities, a contribution from rapidly rotating massive stars with initial rotational velocities as high as 300 km s−1. A mix of rotational velocities may provide a more physical solution than the sole use of massive stars with vrot = 300 km s−1, which are predicted to overestimate the fluorine and average s-process elemental abundances at [Fe/H] ≳ −1. The contribution from AGB stars is predicted to start at [Fe/H] ≈ −1 and becomes increasingly important at high metallicity, being strictly coupled to the evolution of the nitrogen abundance. Finally, by using modern yield sets, we investigate the fluorine abundances of Wolf–Rayet winds, ruling them out as dominant contributors to the galactic fluorine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".