The Temporal and Geographic Extent of Seasonal Cold Trapping on the Moon
Bibliographic record
Abstract
Abstract We assess the geographic distribution and temporal variability of seasonal shadow at the lunar polar regions and explore its influence on surface water migration and deposition within known permanently shadowed regions (PSRs) in the modern era. At its largest expanse near the winter solstice, seasonally shadowed area more than doubles the permanently shadowed area at both poles. The growth and decay of polar shadow throughout the year enforce distinct seasonal patterns in the poleward migration of water as well as a cyclical variation in the polar surface hydration throughout the year if a continual source of water is assumed. The polar surface water abundance peaks near the hemispheric vernal equinox—significantly offset from the solstice where the seasonal trapping area is most expansive—due to the retention of seasonally trapped water. Owing to their low areal density, lower‐latitude PSRs do not significantly hamper the poleward migration of water, enabling water to reach the high polar latitudes where cold trapping area is densest. We find that northern hemisphere PSRs accumulate more water per unit area than southern hemisphere PSRs and that this disparity is especially prominent beyond 85°. The north/south asymmetry is attributed to differences in the hemispheric PSR size‐frequency distributions; such differences enable unique north/south migration diffusivities, which favor more water reaching the high northern latitudes.
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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".