Global Distribution of Lunar Impact Melt Flows
Bibliographic record
Abstract
In this study, we analyzed the distribution and properties of 146 craters with impact melt deposits exterior to their rims. Many of these craters were only recently discovered due to their unusual radar properties in the near-global Mini-RF data set. We find that most craters with exterior deposits of impact melt are small, less than 20 kilometers, and that the smallest craters have the longest melt flows relative to their size. In addition, exterior deposits of impact melt are more common in the highlands than the mare. This may be the result of differing target properties in the highlands and mare, the difference in titanium content, or the greater variation of topography in the highlands. We find that 80 percent of complex craters and 60 percent of simple craters have melt directions that are coincident or nearly coincident with the lowest point in their rim, implying that pre-existing topography plays a dominant role in melt emplacement. This is likely due to movement during crater modification (complex craters) or breached crater rims (simple craters). We also find that impact melt flows have very high circular polarization ratios compared to other features on the Moon. This suggests that their surfaces are some of the roughest material on the Moon at the centimeter to decimeter scale, even though they appear smooth at the meter scale.
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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 | 0.000 | 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".