RAVE stars in K2
Bibliographic record
Abstract
We present a set of 87 RAVE stars with detected solar like oscillations, observed during Campaign 1 of the K2 mission (RAVE K2-C1 sample). This dataset provides a useful benchmark for testing the gravities provided in RAVE Data Release 4 (DR4), and is key for the calibration of the RAVE Data Release 5 (DR5). In the present work, we use two different pipelines, GAUFRE (Valentini et al. 2013) and Sp_Ace (Boeche & Grebel 2016), to determine atmospheric parameters and abundances by fixing log(g) to the seismic one. Our strategy ensures highly consistent values among all stellar parameters, leading to more accurate chemical abundances. A comparison of the chemical abundances obtained here with and without the use of seismic log(g) information has shown that an underestimated (overestimated) gravity leads to an underestimated (overestimated) elemental abundance (e.g. [Mg/H] is underestimated by ~0.25 dex when the gravity is underestimated by 0.5 dex). We then perform a comparison between the seismic gravities and the spectroscopic gravities presented in the RAVE DR4 catalogue, extracting a calibration for log(g) of RAVE giants in the colour interval 0.50
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".