Detailed Morphologic Mapping and Traverse Planning for a Rover-based Lunar Sample Return Mission to Schrödinger Basin
Bibliographic record
Abstract
Abstract Schrödinger basin, a 312 km diameter complex impact structure located near the lunar south pole, has been widely cited as a prime target for future lunar exploration. In 2020 NASA identified Schrödinger as a high-priority landing site for a 2024 mission supported by the Payloads and Research Investigations on the Surface of the Moon solicitation and the Commercial Lunar Payload Services program. Schrödinger basin hosts an uplifted peak ring that would provide a surface mission with access to materials that originated deep within the lunar crust, as well as material ejected from the larger South Pole–Aitken basin. Schrödinger basin also hosts well-preserved mare and pyroclastic deposits that could provide valuable insight into volcanic processes on the Moon. This study used high-resolution Wide Angle Camera and Narrow Angle Camera images from the Lunar Reconnaissance Orbiter, elevation data from the Lunar Orbiter Laser Altimeter, and spectral data from the Clementine mission to produce a high-resolution morphologic map of the basin center consisting of 10 distinct morphologic units. This new map was used to plan traverse paths for a rover mission to the region. The design requirements for this traverse were based on those originally developed for the multiagency Human-Enhanced Robotic Architecture and Capability for Lunar Exploration and Science (HERACLES) mission and the Canadian Space Agency Precursor to Humans And Science Rover concept. The proposed traverse path includes up to 20 sample collection stops with the goal of better understanding lunar chronology, lunar volcanism, and the impact cratering process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".