Testing Mars-inspired operational strategies for semi-autonomous rovers on the Moon: The GeoHeuristic Operational Strategies Test in New Mexico.
Bibliographic record
Abstract
BACKGROUND: We tested the science operational strategy used for the Mars Exploration Rover (MER) mission on Mars to determine its suitability for conducting remote geology on the Moon by conducting a field test at Cerro de Santa Clara, New Mexico. This region contains volcanic and sedimentary products from a variety of provenances, mimicking the variety that might be found at a lunar site such as South Pole-Aitken Basin. METHOD: At each site a Science Team broke down observational "days" into a sequence of observations of features and targets of interest. The number, timing, and sequence of observations was chosen to mimic those used by the MERs when traversing. Images simulating high-resolution stereo and hand lens-scale images were taken using a professional SLR digital camera; multispectral and XRD data were acquired from samples to mimic the availability of geochemical data. A separate Tiger Team followed the Science Team and examined each site using traditional terrestrial field methods, facilitating comparison between what was revealed by human versus rover-inspired methods. LESSONS LEARNED: We conclude from this field test that MER-inspired methodology is not conducive to utilizing all acquired data in a timely manner for the case of any lunar architecture that involves the acquisition of rover data in near real-time. We additionally conclude that a methodology similar to that used for MER can be adapted for use on the Moon if mission goals are focused on reconnaissance. If the goal is to locate and identify a specific feature or material, such as water ice, a different methodology will likely be needed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".