Ice Shell Structure of Ganymede and Callisto Based on Impact Crater Morphology
Bibliographic record
Abstract
Abstract Understanding the thermal structure of the ice shells around Ganymede and Callisto remains a critical step in unraveling the geologic histories of the moons. The depth‐diameter (d‐D) trends of pristine craters on each surface have an inflection point in crater morphology at approximately 26 km diameter, at which point observed crater depths transition from craters deepening with increasing crater diameter to craters shallowing with increasing crater diameter. In this work, we use iSALE‐2D to simulate impact crater formation in ice shells. We test conductive thermal gradients between 5 and 15 K/km and convective ice temperatures between 240 and 260 K in a modeled ice shell to determine if these observed d‐D trends correlate to specific thermal parameters. We find that the conductive thermal gradient has a more pronounced effect than the temperature of the underlying convective ice on reproducing the d‐D trends of craters up to 100 km in diameter, and that the inflection point in the observed crater depth trend on both Ganymede and Callisto is replicated in ice shells with a conductive thermal gradient of ∼10 K/km. With this conductive thermal gradient, the conductive ice was approximately 12–14 km thick on each body at the time they accrued these unmodified craters. The similar thermal constraints for both moons suggest that there are other differences between Ganymede and Callisto responsible for their divergent evolutionary paths, such as an increased proportion of strong impurities in Callisto's ice shell.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".