Lunar Cold Spots and Crater Production on the Moon
Bibliographic record
Abstract
Abstract Mapping of lunar nighttime surface temperatures has revealed anomalously low nighttime temperatures around recently formed impact craters on the Moon. The thermophysically distinct “cold spots” provide a way of identifying the most recently formed impact craters. Over 2,000 cold spot source craters were measured with diameters ranging from 43 m to 2.3 km. Comparison of the crater size‐frequency distribution with crater chronology models and crater counts of superposed craters on the ejecta of the largest cold spot craters constrains the retention time of the cold spots to no more than ~0.5–1.0 Myr with smaller cold spots possibly retained for only few hundred kyr. This would suggest a relatively rapid impact gardening rate with regolith overturn depths exceeding ~5 cm over this time scale. We observe a longitudinal heterogeneity in the cold spot distribution that reflects the Moon's synchronous rotation with a higher density of cold spots at the apex of motion. The magnitude of the asymmetry indicates the craters formed from a population of objects with low mean encounter velocities ~8.4 km/s. The larger cold spots ( D > 800 m) do not follow this trend, and are concentrated on the trailing farside. This could result from a shorter retention time for larger cold spots on the leading hemisphere due to the greater number of smaller, superposed impacts. Alternatively, the abundance of large cold spots on the trailing farside resulted from a swarm of 100‐m‐scale impactors striking the Moon within the last ~0.5 Myr.
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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 | 0.001 | 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".