THE RAVE SURVEY: RICH IN VERY METAL-POOR STARS
Bibliographic record
Abstract
Very metal-poor stars are of obvious importance for many problems in chemical evolution, star formation, and galaxy evolution. Finding complete samples of such stars which are also bright enough to allow high-precision individual analyses is of considerable interest. We demonstrate here that stars with iron abundances [Fe/H] <−2 dex, and down to below −4 dex, can be efficiently identified within the Radial Velocity Experiment (RAVE) survey of bright stars, without requiring additional confirmatory observations. We determine a calibration of the equivalent width of the calcium triplet lines measured from the RAVE spectra onto true [Fe/H], using high spectral resolution data for a subset of the stars. These RAVE iron abundances are accurate enough to obviate the need for confirmatory higher-resolution spectroscopy. Our initial study has identified 631 stars with [Fe/H] ⩽−2, from a RAVE database containing approximately 200,000 stars. This RAVE-based sample is complete for stars with [Fe/H] ≲−2.5, allowing statistical sample analysis. We identify three stars with [Fe/H] ≲−4. Of these, one was already known to be "ultra metal-poor," one is a known carbon-enhanced metal-poor star, but we obtain [Fe/H] = −4.0, rather than the published [Fe/H] = −3.3, and derive [C/Fe] = +0.9, and [N/Fe] = +3.2, and the third is at the limit of our signal-to-noise ratio. RAVE observations are ongoing and should prove to be a rich source of bright, easily studied, very metal-poor stars.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".