Manganese in dwarfspheroidal galaxies
Bibliographic record
Abstract
We provide manganese abundances (corrected for the effect of the hyperfine structure) for a large number of stars in the dwarf spheroidal galaxies Sculptor and Fornax, and for a smaller number in the Carina and Sextans dSph galaxies. Abundances had already been determined for a number of other elements in these galaxies, including α and iron-peak ones, which allowed us to build [Mn/Fe] and [Mn/α] versus [Fe/H] diagrams. The Mn abundances imply sub-solar [Mn/Fe] ratios for the stars in all four galaxies examined. In Sculptor, [Mn/Fe] stays roughly constant between [Fe/H] ~ −1.8 and −1.4 and decreases at higher iron abundance. In Fornax, [Mn/Fe] does not vary in any significant way with [Fe/H]. The relation between [Mn/α] and [Fe/H] for the dSph galaxies is clearly systematically offset from that for the Milky Way, which reflects the different star formation histories of the respective galaxies. The [Mn/α] behavior can be interpreted as a result of the metal-dependent Mn yields of Type II and Type Ia supernovae. We also computed chemical evolution models for star formation histories matching those determined empirically for Sculptor, Fornax, and Carina, and for the Mn yields of SNe Ia, which were assumed to be either constant or variable with metallicity. The observed [Mn/Fe] versus [Fe/H] relation in Sculptor, Fornax, and Carina can be reproduced only by the chemical evolution models that include a metallicity-dependent Mn yield from the SNe Ia.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".