Determining best practices in reconnoitering sites for habitability potential on Mars using a semi-autonomous rover: A GeoHeuristic Operational Strategies Test
Bibliographic record
Abstract
We tested science operations strategies developed for use in remote mobile spacecraft missions, to determine whether reconnoitering a site of potential habitability prior to in-depth study (a walkabout-first strategy) can be a more efficient use of time and resources than the linear approach commonly used by planetary rover missions. Two field teams studied a sedimentary sequence in Utah to assess habitability potential. At each site one team commanded a human "rover" to execute observations and conducted data analysis and made follow-on decisions based solely on those observations. Another team followed the same traverse using traditional terrestrial field methods, and the results of the two teams were compared. Test results indicate that for a mission with goals similar to our field case, the walkabout-first strategy may save time and other mission resources, while improving science return. The approach enabled more informed choices and higher team confidence in choosing where to spend time and other consumable resources. The walkabout strategy may prove most efficient when many close sites must be triaged to a smaller subset for detailed study or sampling. This situation would arise when mission goals include finding, identifying, characterizing or sampling a specific material, feature or type of environment within a certain area.
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".