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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".