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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".