Notional Geological Traverses, Station Activities, and Sample Collection on Mons Malapert, Lunar South Polar Region
Bibliographic record
Abstract
Abstract The geology of a potential Artemis landing site on Mons Malapert is examined using remote sensing techniques and lessons learned from Apollo missions to the lunar surface. Orthomosaics, digital terrain models, illumination models, thermal conditions, crater size‐frequency distribution analyses, geomorphological mapping, spectral and compositional analyses, lunar surface physical property analyses, and image processing to reveal the lunar surface within permanently shadowed regions (PSRs) were integrated with anticipated crew capabilities to develop three notional extravehicular activity (EVA) traverses lasting 3, 3, and 6 hr each. The traverse plans recover 43 samples, with a mass of 44 kg, secured in 54 kg of sample containers, including those NASA requires for samples collected in and around PSRs, which is within the 100 kg limit NASA allows for a landed mission. The geologic plan for the EVAs addresses seven Artemis III science objectives, 25 science goals within those objectives, and 90 specific investigations of varying priority in the Artemis III Science Definition Team Report (43 high‐, 40 medium‐, 2 medium‐high‐, and 5 low‐priority investigations); for example, test and reveal new details about the lunar magma ocean hypothesis, the basin‐forming epoch and implications for Solar System architecture, sources and distribution of volatiles, and regolith physical properties relevant to human and robotic exploration.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".