Topography‐Enhanced Ultra‐Cold Trapping at the LCROSS Impact Site
Bibliographic record
Abstract
Abstract Small topographic features below the resolution of existing orbital data sets may create “micro ultra‐cold traps” within the larger permanently shadowed regions that are present at the lunar poles. These ultra‐cold traps are protected from the major primary and secondary illumination sources, and thus would create surfaces that are much colder than lower‐resolution temperature maps would indicate. We examine this effect by creating a high resolution (1 m pix −1 ) terrain map based on upscaled data from the Lunar Orbiter Laser Altimeter. This map is illuminated by scattered sunlight and infrared emissions from sunlit terrain, which are then run through a thermal model to determine temperatures. We find that while most of the terrain experiences maximum temperatures around 50 K, there are a number of 1–30 m‐scale ultra‐cold traps with maximum temperatures as low as 20–30 K. By comparing our modeled ultra‐cold trapping area to volatile abundances measured by Lunar Crater Observation and Sensing Satellite (LCROSS), we reveal a diverse environment where the surficial abundances necessary to explain the LCROSS results are strongly dependent on precisely where the impact occurred.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".