Bibliographic record
Abstract
Kepler planets (including super-Earths and sub-Neptunes) are likely formed before the gaseous proto-planetary disks have dissipated. Together with gas giants, we call these generation-I planets, to differentiate them from planets that form after disk dispersal (generation-II planets, e.g., terrestrial planets in the Solar system). If the metal content in these disks resembles that in the host stars, one naively expects Kepler planets to occur more frequently, and to be more massive, around metal-rich stars. Contrary to these expectations, we find that the radii of Kepler planets (a proxy for mass) are independent of host metallicity, and their occurrence rate rises only weakly with metallicity. The latter trend is further flattened when the influence of close binaries is accounted for. We interpret the first result as that the mass of a Kepler planet is regulated by a yet unknown process, as first suggested by \citet{Wu2019}. We explain the second result using a simple model, wherein the masses of proto-planetary disks have a much larger spread than the spread in stellar metallicity, and disks that contain more than $\sim 30$ Earth masses of total solid can form Kepler planets. Hosts for these planets, as a result, are only mildly more metal-rich than average. In contrast, the formation of a giant planet requires some $5$ times more solid. Their hosts, which also harbour Kepler planets, are significantly more metal-rich. This model also predicts that stars more metal-poor than half-solar should rarely host any gen-I planets.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".