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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".