The Pristine Inner Galaxy Survey (PIGS)
Bibliographic record
Abstract
The most metal-poor stars provide valuable insights into the early chemical enrichment history of a system, carrying the chemical imprints of the first generations of supernovae. The most metal-poor region of the Sagittarius dwarf galaxy remains inadequately observed and characterised. To date, only ∼4 stars with [Fe/H] < −2.0 have been chemically analysed with high-resolution spectroscopy. In this study, we present the most extensive chemical abundance analysis of 12 low-metallicity stars with metallicities down to [Fe/H] = −3.26 and located in the main body of Sagittarius. These targets, selected from the Pristine Inner Galaxy Survey, were observed using the MIKE high-resolution spectrograph at the Magellan-Clay telescope, which allowed us to measure up to 17 chemical species. The chemical composition of these stars reflects the imprint of a variety of type II supernovae (SNe II). A combination of low- to intermediate-mass high-energy SNe and hypernovae (∼10 − 70 M ⊙ ) is required to account for the abundance patterns of the lighter elements up to the Fe-peak. The trend of the heavy elements suggests the involvement of compact binary merger events and fast-rotating (up to ∼300 km s −1 ) intermediate-mass to massive metal-poor stars (∼25 − 120 M ⊙ ) that are the sources of rapid and slow processes, respectively. Additionally, asymptotic giant branch stars contribute to a wide dispersion of [Ba/Mg] and [Ba/Eu]. The absence of an α −knee in our data indicates that type Ia supernovae did not contribute in the very metal-poor region ([Fe/H] ≤ −2.0). However, they might have started to pollute the interstellar medium at [Fe/H] > −2.0, given the relatively low [Co/Fe] in this metallicity region.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".