INSIGHTS INTO PRE-ENRICHMENT OF STAR CLUSTERS AND SELF-ENRICHMENT OF DWARF GALAXIES FROM THEIR INTRINSIC METALLICITY DISPERSIONS
Bibliographic record
Abstract
Star clusters are known to have smaller intrinsic metallicity spreads than dwarf galaxies due to their shorter star formation timescales. Here we use individual spectroscopic [Fe/H] measurements of stars in 19 Local Group dwarf galaxies, 13 Galactic open clusters, and 49 globular clusters to show that star cluster and dwarf galaxy linear metallicity distributions are binomial in form, with all objects showing strong correlations between their mean linear metallicity $\bar{Z}$ and intrinsic spread in metallicity $\sigma(Z)^{2}$. A plot of $\sigma(Z)^{2}$ versus $\bar{Z}$ shows that the correlated relationships are offset for the dwarf galaxies from the star clusters. The common binomial nature of these linear metallicity distributions can be explained with a simple inhomogeneous chemical evolution model (e.g., Oey 2000), where the star cluster and dwarf galaxy behaviour in the $\sigma(Z)^{2}-\bar{Z}$ diagram is reproduced in terms of the number of enrichment events, covering fraction, and intrinsic size of the enriched regions. The inhomogeneity of the self-enrichment sets the slope for the observed dwarf galaxy $\sigma(Z)^{2}-\bar{Z}$ correlation. The offset of the star cluster sequence from that of the dwarf galaxies is due to pre-enrichment, and the slope of the star cluster sequence represents the remnant signature of the self-enriched history of their host galaxies. The offset can be used to separate star clusters from dwarf galaxies without a priori knowledge of their luminosity or dynamical mass. The application of the inhomogeneous model to the $\sigma(Z)^{2}-\bar{Z}$ relationship provides a numerical formalism to connect the self-enrichment and pre-enrichment between star clusters and dwarf galaxies using physically motivated chemical enrichment parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".