Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog with Measured Completeness and Reliability Based on Data Release 25
Bibliographic record
Abstract
's detection limit, which includes temperate, super-Earth size planets around GK dwarf stars in our Galaxy. This paper discusses the Robovetter and the metrics it uses to decide which TCEs are called planet candidates in the DR25 KOI catalog. We also discuss the simulated transits, simulated systematic noise, and simulated astrophysical false positives created in order to characterize the properties of the final catalog. For orbital periods less than 100 d the Robovetter completeness (the fraction of simulated transits that are determined to be planet candidates) across all observed stars is greater than 85%. For the same period range, the catalog reliability (the fraction of candidates that are not due to instrumental or stellar noise) is greater than 98%. However, for low signal-to-noise candidates found between 200 and 500 days, our measurements indicate that the Robovetter is 73.5% complete and 37.2% reliable across all searched stars (or 76.7% complete and 50.5% reliable when considering just the FGK dwarf stars). We describe how the measured completeness and reliability varies with period, signal-to-noise, number of transits, and stellar type. Also, we discuss a value called the disposition score which provides an easy way to select a more reliable, albeit less complete, sample of candidates. The entire KOI catalog, the transit fits using Markov chain Monte Carlo methods, and all of the simulated data used to characterize this catalog are available at the NASA Exoplanet Archive.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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 | 0.005 |
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".