Observing the metal-poor solar neighbourhood: a comparison of galactic chemical evolution predictions*†
Bibliographic record
Abstract
Atmospheric parameters and chemical compositions for ten stars with metallicities in the region of -2.2< [Fe/H] <-0.6 were precisely determined using high resolution, high signal to noise, spectra. For each star the abundances, for 14 to 27 elements, were derived using both LTE and NLTE approaches. In particular, differences by assuming LTE or NLTE are about 0.10 dex; depending on [Fe/H], Teff, gravity and element lines used in the analysis. We find that the O abundance has the largest error, ranging from 0.10 and 0.2 dex. The best measured elements are Cr, Fe, and Mn; with errors etween 0.03 and 0.11 dex. The stars in our sample were included in previous different observational work. We provide a consistent data analysis. The data dispersion introduced in the literature by different techniques and assumptions used by the different authors is within the observational errors, excepting for HD103095. We compare these results with stellar observations from different data sets and a number of theoretical galactic chemical evolution (GCE) simulations. We find a large scatter in the GCE results, used to study the origin of the elements. Within this scatter as found in previous GCE simulations, we cannot reproduce the evolution of the elemental ratios [Sc/Fe], [Ti/Fe], and [V/Fe] at different metallicities. The stellar yields from core collapse supernovae (CCSN) are likely primarily responsible for this discrepancy. Possible solutions and open problems are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".