Complex crater formation by low energy impactors
Bibliographic record
Abstract
We investigate the formation of complex craters in low-energy laboratory impacts using layered granular beds and a range of impactors, including solid, liquid, and granular types. Shallow granular targets change how the impact energy is dissipated, resulting in power-law scalings for the crater diameter that depart from those observed in homogeneous targets. An adaptation of the well-known Schmidt-Holsapple scaling was made to explain the impacts made from the liquid droplets. Furthermore, we show that the layered target promotes the formation of complex crater features, including flat floors and central peaks, even at low impact energies, through an essentially distinct process when compared to high energy impacts. In particular, granular impactors consistently produce ring-shaped craters, a result explained by a mechanism analogous to air entrapment in droplet impacts. This ring-like morphology was also successfully reproduced in simulations using a modelling approach developed in this work. These findings suggest that layered targets can reproduce features typical of planetary-scale complex craters at the laboratory scale, opening new avenues for small-scale experimental studies of impact dynamics with potential applications in planetary geology and civil engineering.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".