Topographic characterization of lunar complex craters
Bibliographic record
Abstract
Abstract We use Lunar Orbiter Laser Altimeter topography data to revisit the depth (d)‐diameter (D), and central peak height (hcp)‐diameter relationships for fresh complex lunar craters. We assembled a data set of young craters with D ≥ 15 km and ensured the craters were unmodified and fresh using Lunar Reconnaissance Orbiter Wide‐Angle Camera images. We used Lunar Orbiter Laser Altimeter gridded data to determine the rim‐to‐floor crater depths, as well as the height of the central peak above the crater floor. We established power‐law d‐D and hcp‐D relationships for complex craters on mare and highlands terrain. Our results indicate that craters on highland terrain are, on average, deeper and have higher central peaks than craters on mare terrain. Furthermore, we find that the crater depths for both mare and highlands craters are significantly deeper than previously reported. This likely reflects the inclusion of transitional craters and/or older and/or modified craters in previous work, as well as the limitations of the stereophotogrammetric and shadow‐length data sets used in those studies. There is substantial variability in the depths and the central peak heights for craters in a given diameter range. We suggest that the differences in mean d and hcp as a function of crater diameter for highlands and mare craters result from differences in bulk physical properties of the terrain types, whereas the variability in d and hcp at a given diameter may reflect variations in impactor properties and impact parameters.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".