Bibliographic record
Abstract
Context. Low-metallicity stellar populations are very abundant in the Universe, either as the remnants of the past history of the Milky Way or similar spiral galaxies, or the young low-metallicity stellar populations that are being observed in the local dwarf galaxies or in the high- z objects with low metal content recently found with JWST. Aims. Our goal is to develop new high-spectral-resolution models tailored for low-metallicity environments and apply them to the analysis of stellar population data, particularly in cases in which a significant portion of the stellar content exhibits low metallicity. Methods. We used the state-of-the-art stellar population synthesis code HR- PY P OP S TAR with available stellar libraries to create a new set of models focused on low-metallicity stellar populations. Results. We compared the new spectral energy distributions with the previous models of HR- PY P OP S TAR for solar metallicity. Once we verified that the spectra, except for the oldest ones that show some differences in the molecular bands of the TiO and G band, are similar, we re-analysed the high-resolution data from the globular cluster M 15 by finding a better estimate of its age and metallicity. Finally, we analysed a sub-sample of mostly star-forming dwarf galaxies from the MaNGA survey, we found a similar stellar mass-mean stellar metallicity weighted by light to other studies that studied star-forming dwarf galaxies and a slightly higher mean stellar metallicity than the other works that analysed all types of dwarf galaxies at the same time, but that are within the error bars.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.169 | 0.159 |
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".