Boulder Distributions Around Young, Small Lunar Impact Craters and Implications for Regolith Production Rates and Landing Site Safety
Bibliographic record
Abstract
Abstract We use Lunar Reconnaissance Orbiter Camera Narrow Angle Camera images to characterize boulder populations around six small (<1 km), young (<200 Ma) impact craters near spacecraft landing sites. The Narrow Angle Camera boulder counts are used to analyze how boulder distributions vary around craters of different sizes and ages. These comparisons inform how various properties affect the distance to which boulders are ejected and the size and density of boulders produced by an impact event. The counts show that boulder population densities decrease with crater age, with few boulders remaining at craters older than a few hundred million years, consistent with results of other studies of boulder degradation rates on the Moon. Variations in boulder distributions around younger craters may provide information regarding impact conditions; South Ray crater has a larger population of small boulders than the larger North Ray crater, which could be explained by variations in impact velocity. Large craters generally excavate more boulders than smaller craters, and the size of the largest boulder ejected is related to crater size by a power‐law function. Larger boulders occur closer to the crater rim (within 2–4 crater radii), whereas smaller boulders occur at all distances. The density of boulders is greater near the crater rim and decreases with increasing radial distance; this data can aid in establishing safe landing zones for future missions. Analyzing boulder distributions across craters of varying ages allows us to test models of boulder breakdown rates, with implications for understanding the Moon's regolith production rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".