Transitional impact craters on the Moon: Insight into the effect of target lithology on the impact cratering process
Bibliographic record
Abstract
Abstract We studied a data set of 28 well‐preserved lunar craters in the transitional (simple‐to‐complex) regime with the aim of investigating the underlying cause(s) for morphological differences of these craters in mare versus highland terrains. These transitional craters range from 15 to 42 km in diameter, demonstrating that the transition from simple to complex craters is not abrupt and occurs over a broad diameter range. We examined and measured the following crater attributes: depth (d), diameter (D), floor diameter (Df), rim height (h), and wall width (w), as well as the number and onset of terraces and rock slides. The number of terraces increases with increasing crater size and, in general, mare craters possess more terraces than highland craters of the same diameter. There are also clear differences in the d/D ratio of mare versus highland craters, with transitional craters in mare targets being noticeably shallower than similarly sized highland craters. We propose that layering in mare targets is a major driver for these differences. Layering provides pre‐existing planes of weakness that facilitate crater collapse, thus explaining the overall shallower depths of mare craters and the onset of crater collapse (i.e., the transition from simple to complex crater morphology) at smaller diameters as compared to highland craters. This suggests that layering and its interplay with target strength and porosity may play a more significant role than previously considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".