The R-Process Alliance: Fourth Data Release from the Search for R-process-enhanced Stars in the Galactic Halo*
Bibliographic record
Abstract
Abstract This compilation is the fourth data release from the R-Process Alliance (RPA) search for r-process-enhanced stars and the second release based on “snapshot” high-resolution (R ∼ 30,000) spectra collected with the du Pont 2.5 m Telescope. In this data release, we propose a new delineation between the r-I and r-II stellar classes at , instead of the empirically chosen level previously in use, based on statistical tests of the complete set of RPA data released to date. We also statistically justify the minimum level of [Eu/Fe] for definition of the r-I stars, [Eu/Fe] > +0.3. Redefining the separation between r-I and r-II stars will aid in the analysis of the possible progenitors of these two classes of stars and determine whether these signatures arise from separate astrophysical sources at all. Applying this redefinition to previous RPA data, the number of identified r-II and r-I stars changes to 51 and 121, respectively, from the initial set of data releases published thus far. In this data release, we identify 21 new r-II, 111 new r-I (plus 3 re-identified), and 7 new (plus 1 re-identified) limited-r stars out of a total of 232 target stars, resulting in a total sample of 72 new r-II stars, 232 new r-I stars, and 42 new limited-r stars identified by the RPA to date.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.012 |
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".