Experimental hypervelocity impact of sedimentary and metamorphic rocks: Reconstruction of crater shapes and volumes
Bibliographic record
Abstract
The shapes and volumes of crater forms have been determined using 3-D laser scan data acquired following the hypervelocity impact of rock. Forty-four shots (23 sedimentary targets and 21 metamorphic targets) at impact velocities from 2.50 to 7.85 km/s have been assessed from the Multidisciplinary Experimental and Modeling Impact Network (MEMIN). Best fits have been determined for parabolic, hyperbolic and power law shapes for the central (penetration) crater, including the hemispheric form for the spall (lateral) crater. For the central craters, both the remnant and reconstructed forms are evaluated. In both cases, the best shape fits are hyperbolic, followed by parabolic then power law. The maximum angles at which the reconstructed central craters intersect the surface are 60-65° for hyperbolic, 80-90° for parabolic and 70-90° for power law. Maximum ejecta angles captured by high-speed video during the experiments are most closely matched by the predicted hyperbolic angles. Reconstructed central crater depth-diameter relations are between 0.20 and 0.40 for most target rocks (neglecting outliers in sedimentary targets), with the ratio being velocity and impact energy invariant. The depth-diameter ratio for the metamorphic targets is more constrained at an average of ∼0.20. For the spall craters, the best shape fits are power law, followed by hyperbolic then parabolic and hemispheric. Spall craters are close to conic in form. With increasing impact energy the contribution of spall to total crater volume increases relative to central crater volume. As the overall best fit for the central crater shape, the hyperbolic form has the highest volume (25% more than the parabolic) and the lowest (broadest spread) ejecta angles. This indicates that for brittle materials in the strength regime a greater ejecta volume would be generated and dispersed over a wider area relative to the other forms, with implications for protective system design and applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".