Strategic Planning Insights for Future Science-Driven Extravehicular Activity on Mars
Bibliographic record
Abstract
Short-term and long-term science plans were developed as part of the strategic planning process used by the Biologic Analog Science Associated with Lava Terrains (BASALT) science team to conduct two Mars-simulation missions investigating basalt habitability at terrestrial volcanic analog sites in 2016. A multidisciplinary team of scientists generated and codified a range of scientific hypotheses distilled into a Science Traceability Matrix (STM) that defined the set of objectives pursued in a series of extravehicular activity (EVA) campaigns performed across multiple field deployments. This STM was used to guide the pre-deployment selection of sampling stations within the selected Mars analog sites on the Earth based on precursor site information such as multispectral imagery. It also informed selection of hand-held instruments and observational data to collect during EVA to aid sample selection through latency-impacted interaction with an Earth-based Science Support Team. A significant portion of the pre-deployment strategic planning activities were devoted to station selection, ultimately the locations used for sample collection and EVA planning. During development of the EVAs, the BASALT science team identified lessons learned that could be used to inform future missions and analog activities, including the critical need for high-resolution precursor imagery that would enable the selection of stations that could meet the scientific objectives outlined in the STM.
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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 | 0.000 |
| 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".