Mercury's inner core size and core crystallization regime
Bibliographic record
Abstract
Abstract Earth-based radar observation of Mercury’s rotation vector combined with gravity observation by the MESSENGER spacecraft yield a measure of Mercury’s moment of inertia and the amplitude of the 88-day libration of its silicate shell. These two geodetic constraints provide information on Mercury’s interior structure, including the presence of a fluid core, the radius of the core–mantle boundary and the bulk densities of the core and mantle. In this work, we show how they further provide information on the size of the solid inner core and on the crystallization regime of the fluid core. If Mercury’s fluid core is a Fe–FeS alloy with a sulfur concentration on the Fe-rich side of the eutectic, the largest inner core compatible with geodetic observations at the 1 σ level is 1325 ± 250 km. Our results further suggest that the crystallization scenario that best fits the geodetic observations involves the formation of Fe-snow within the fluid core, and that this scenario is preferred for models with an iron-poor mantle composition. Consequently, Mercury’s dynamo most likely operates in concert with snow formation. For an inner core larger than ∼650 km, snow formation extends to the inner core boundary. If a dynamo cannot be maintained by the dynamics of snow formation, or if such dynamo produces a magnetic field incompatible with observation, Mercury’s inner core must then be smaller than 650 km.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".