The Apokasc Catalog: An Asteroseismic and Spectroscopic Joint Survey of Targets in the Kepler Fields
Bibliographic record
Abstract
We present the first APOKASC catalog of spectroscopic and asteroseismic properties of 1916 red giants observed in the Kepler fields. The spectroscopic parameters provided from the Apache Point Observatory Galactic Evolution Experiment project are complemented with asteroseismic surface gravities, masses, radii, and mean densities determined by members of the Kepler Asteroseismology Science Consortium. We assess both random and systematic sources of error and include a discussion of sample selection for giants in the Kepler fields. Total uncertainties in the main catalog properties are of the order of 80 K in Teff, 0.06 dex in [M/ H], 0.014 dex in log g, and 12% and 5% in mass and radius, respectively; these reflect a combination of systematic and random errors. Asteroseismic surface gravities are substantially more precise and accurate than spectroscopic ones, and we find good agreement between their mean values and the calibrated spectroscopic surface gravities. There are, however, systematic underlying trends with Teff and log g. Our effective temperature scale is between 0 and 200 K cooler than that expected from the infrared flux method, depending on the adopted extinction map, which provides evidence for a lower value on average than that inferred for the Kepler Input Catalog (KIC). We find a reasonable correspondence between the photometric KIC and spectroscopic APOKASC metallicity scales, with increased dispersion in KIC metallicities as the absolute metal abundance decreases, and offsets in T-eff and log g consistent with those derived in the literature. We present mean fitting relations between APOKASC and KIC observables and discuss future prospects, strengths, and limitations of the catalog data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| 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) | 0.006 | 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".