A mineralogical reason why all exoplanets cannot be equally oxidizing
Bibliographic record
Abstract
ABSTRACT From core to atmosphere, the oxidation states of elements in a planet shape its character. Oxygen fugacity ($f_{\rm O_2}$) is one parameter indicating these likely oxidation states. The ongoing search for atmospheres on rocky exoplanets benefits from understanding the plausible variety of their compositions, which depends strongly on their oxidation states – and if derived from interior outgassing, on the $f_{\rm O_2}$ at the top of their silicate mantles. This $f_{\rm O_2}$ must vary across compositionally diverse exoplanets, but for a given planet, its value is unconstrained insofar as it depends on how iron (the dominant multivalent element) is partitioned between its 2+ and 3+ oxidation states. Here, we focus on another factor influencing how oxidizing a mantle is – a factor modulating $f_{\rm O_2}$ even at fixed Fe3+/Fe2+ – the planet’s mineralogy. Only certain minerals (e.g. pyroxenes) incorporate Fe3+. Having such minerals in smaller mantle proportions concentrates Fe3+, increasing $f_{\rm O_2}$. Mineral proportions change within planets according to pressure, and between planets according to bulk composition. Constrained by observed host star refractory abundances, we calculate a minimum $f_{\rm O_2}$ variability across exoplanet mantles, of at least two orders of magnitude, due to mineralogy alone. This variability is enough to alter by a hundredfold the mixing ratio of SO2 directly outgassed from these mantles. We further predict that planets orbiting high-Mg/Si stars are more likely to outgas detectable amounts of SO2 and H2O; and for low-Mg/Si stars, detectable CH4, all else equal. Even absent predictions of Fe3+ budgets, general insights can be obtained into how oxidizing an exoplanet’s mantle is.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".