Chemical diversity of super-Earths as a consequence of formation
Bibliographic record
Abstract
ABSTRACT Recent observations of rocky super-Earths have revealed an apparent wider distribution of Fe/Mg ratios, or core to mantle ratios, than the planets in our Solar system. This study aims to understand how much of the chemical diversity in the super-Earth population can arise from giant impacts during planetary formation. Planet formation simulations have only recently begun to treat collisions more realistically in an attempt to replicate the planets in our Solar system. We investigate planet formation more generally by simulating the formation of rocky super-Earths with varying initial conditions using a version of symba, a gravitational N-body code, that incorporates realistic collisions. We track the maximum plausible change in composition after each impact. The final planets span a range of Fe/Mg ratios similar to the Solar system planets, but do not completely match the distribution in super-Earth data. We only form a few planets with minor iron-depletion, suggesting other mechanisms are at work. The most iron-rich planets have a lower Fe/Mg ratio than Mercury, and are less enriched than planets such as Kepler-100b. This indicates that further work on our understanding of planet formation and further improvement of precision of mass and radius measurements are required to explain planets at the extremes of this Fe/Mg distribution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".