Effect of impact velocity and acoustic fluidization on the simple‐to‐complex transition of lunar craters
Bibliographic record
Abstract
Abstract We use numerical modeling to investigate the combined effects of impact velocity and acoustic fluidization on lunar craters in the simple‐to‐complex transition regime. To investigate the full scope of the problem, we employed the two widely adopted Block‐Model of acoustic fluidization scaling assumptions (scaling block size by impactor size and scaling by coupling parameter) and compared their outcomes. Impactor size and velocity were varied, such that large/slow and small/fast impactors would produce craters of the same diameter within a suite of simulations, ranging in diameter from 10 to 26 km, which straddles the simple‐to‐complex crater transition on the Moon. Our study suggests that the transition from simple to complex structures is highly sensitive to the choice of the time decay and viscosity constants in the Block‐Model of acoustic fluidization. Moreover, the combination of impactor size and velocity plays a greater role than previously thought in the morphology of craters in the simple‐to‐complex size range. We propose that scaling of block size by impactor size is an appropriate choice for modeling simple‐to‐complex craters on planetary surfaces, including both varying and constant impact velocities, as the modeling results are more consistent with the observed morphology of lunar craters. This scaling suggests that the simple‐to‐complex transition occurs at a larger crater size, if higher impact velocities are considered, and is consistent with the observation that the simple‐to‐complex transition occurs at larger sizes on Mercury than Mars.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".