The challenge of forming a fuzzy core in Jupiter
Bibliographic record
Abstract
Recent structure models of Jupiter that match Juno gravity data suggest that the planet harbours an extended region in its deep interior that is enriched with heavy elements: a so-called dilute or fuzzy core. This finding raises the question of what possible formation pathways could have lead to such a structure. We modelled Jupiter’s formation and long-term evolution, starting at late-stage formation before runaway gas accretion. The formation scenarios we considered include both primordial composition gradients, as well as gradients that are built as proto-Jupiter rapidly acquires its gaseous envelope. We then followed Jupiter’s evolution as it cools down and contracts, with a particular focus on the energy and material transport in the interior. We find that none of the scenarios we consider lead to a fuzzy core that is compatible with interior structure models. In all the cases, most of Jupiter’s envelope becomes convective and fully mixed after a few million years at most. This is true even when we considered a case where the gas accretion leads to a cold planet, and large amounts of heavy elements are accreted. We therefore conclude that it is very challenging to explain Jupiter’s dilute core from standard formation models. We suggest that future works should consider more complex formation pathways as well as the modelling of additional physical processes that could lead to Jupiter’s current-state internal structure.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".