Using Science-Driven Analog Research to Investigate Extravehicular Activity Science Operations Concepts and Capabilities for Human Planetary Exploration
Bibliographic record
Abstract
Biologic Analog Science Associated with Lava Terrains (BASALT) is a science-driven exploration program seeking to determine the best tools, techniques, training requirements, and execution strategies for conducting Mars-relevant field science under spaceflight mission conditions. BASALT encompasses Science, Science Operations, and Technology objectives. This article outlines the BASALT Science Operations background, strategic research questions, study design, and a portion of the results from the second field test. BASALT field tests are used to iteratively develop, integrate, test, evaluate, and refine new concepts of operations (ConOps) and capabilities that enable efficient and productive science. This article highlights the ConOps investigated during BASALT in light of future planetary extravehicular activity (EVA), which will focus on scientific exploration and discovery, and serves as an introduction to integrating exploration flexibility with operational rigor, the value of tactical and strategic science planning and execution, and capabilities that enable and enhance future science EVA operations.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".