The Pristine survey – XV. A CFHT ESPaDOnS view on the Milky Way halo and disc populations
Bibliographic record
Abstract
ABSTRACT We present a one-dimensional, local thermodynamic equilibrium homogeneous analysis of 132 stars observed at high resolution with ESPaDOnS. This represents the largest sample observed at high resolution (R ∼ 40 000) from the Pristine survey. This sample is based on the first version of the Pristine catalogue and covers the full range of metallicities from [Fe/H] ∼−3 to ∼+0.25, with nearly half of our sample (58 stars) composed of very metal-poor (VMP) stars ([Fe/H] ≤ −2). This wide range of metallicities provides the opportunity of a new detailed study of the Milky Way stellar population. Because it includes both dwarf and giant stars, it also enables the analysis of any potential bias induced by the Pristine selection process. Based on Gaia EDR3, the orbital analysis of this Pristine-ESPaDOnS sample shows that it is composed of 65 halo stars and 67 disc stars. After a general assessment of the sample chemical properties with the α-elements Mg and Ca, we focus on the abundance of carbon and the neutron capture elements Ba and Sr. While most of our VMP subsample is carbon normal, we also find that 14 stars out of the 38 stars with [Fe/H] ≤ −2 and measured carbon abundances turn out to be carbon-enhanced metal-poor (CEMP) stars. We show that these CEMP stars are nearly exclusively (i.e. 12 stars out of 14) in the regime of low luminosity, unevolved, dwarf stars, which we interpret as the consequence of bias of the Pristine filter against C-rich giants. Among the VMP stars, we identify two CEMP stars with no enhancement in neutron-capture process elements and another one enriched in s-process element. Finally, one VMP star is found with a very low [Sr/Fe] abundance ratio for its metallicity, as expected if it had been accreted from an ultra-faint dwarf galaxy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".