The Pristine survey – VI. The first three years of medium-resolution follow-up spectroscopy of Pristine EMP star candidates
Bibliographic record
Abstract
ABSTRACT We present the results of a 3-yr long, medium-resolution spectroscopic campaign aimed at identifying very metal-poor stars from candidates selected with the CaHK, metallicity-sensitive Pristine survey. The catalogue consists of a total of 1007 stars, and includes 146 rediscoveries of metal-poor stars already presented in previous surveys, 707 new very metal-poor stars with $\rm [Fe/H] \lt -2.0$, and 95 new extremely metal-poor stars with $\rm [Fe/H] \lt -3.0$. We provide a spectroscopic [Fe/H] for every star in the catalogue, and [C/Fe] measurements for a subset of the stars (10 per cent with $\rm [Fe/H] \lt -3$ and 24 per cent with $-3 \lt \rm [Fe/H] \lt -2$) for which a carbon determination is possible, contingent mainly on the carbon abundance, effective temperature and signal-to-noise ratio of the stellar spectra. We find an average carbon enhancement fraction ([C/Fe] ≥ +0.7) of 41 ± 4 per cent for stars with $-3 \lt \rm [Fe/H] \lt -2$ and 58 ± 14 per cent for stars with $\rm [Fe/H] \lt -3$, and report updated success rates for the Pristine survey of 56 per cent and 23 per cent to recover stars with $\rm [Fe/H] \lt -2.5$ and $\lt -3$, respectively. Finally, we discuss the current status of the survey and its preparation for providing targets to upcoming multi-object spectroscopic surveys such as William Herschel Telescope Enhanced Area Velocity Explorer.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".