The Pristine survey – XXII. A serendipitous discovery of an extremely Li-rich very metal-poor giant and a new method of 6Li/7Li isotope measurement
Bibliographic record
Abstract
ABSTRACT We report the serendipitous discovery of a very metal-poor (VMP) Li-rich giant star (Teff = 4690 ± 80 K, log g = 1.34 ± 0.13, [Fe/H] = −2.43 ± 0.07). We analyse the Li i 6103 and 6707 Å lines accounting for departures from local thermodynamic equilibrium (NLTE) and correcting for 3D effects using literature data, which yields a lithium abundance log εLi = 3.42 ± 0.07. Comparing lithium abundances from the two lines, in 1D NLTE we measure the isotope ratio 6Li/7Li = 1.64$^{+1.49}_{-1.08}$ per cent. When correcting for 3D effects, we detect the fragile 6Li isotope at 2-sigma level and the ratio 6Li/7Li = 5.65$^{+5.05}_{-2.51}$ per cent. To our knowledge, this is the first 6Li/7Li measurement in an extremely Li-rich VMP star. The Cameron–Fowler mechanism, which is proposed to produce Li-rich stars, does not imply 6Li production and is therefore inconsistent with our measurement when applying 3D corrections. We also derive NLTE abundances for 16 elements, most of which show similar abundances to those found in VMP stars. Sodium is an exception: [Na/Fe]NLTE1D = 0.07 ± 0.03, which is 0.5 dex higher than what is typical for VMP stars. This star joins the sample of rare Li-rich VMP stars, and we offer a novel way to constrain the source of lithium in such stars through isotope ratio measurements.
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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".