Empirical requirements analysis for Mars surface operations using
Bibliographic record
Abstract
Contact: bclancey @ mail.arc.nasa, gov Living and working on Mars will require model-based computer systems for maintaining and controlling complex life support, communication, transportation, and power systems. This technology must work properly on the first three-year mission, augmenting human autonomy, without adding yet more complexity to be diagnosed and repaired. One design method is to work with scientists in analog (Mars-like) settings to understand how they prefer to work, what constraints will be imposed by the Mars environment, and how to ameliorate difficulties. We describe how we are using empirical requirements analysis to prototype model-based tools at a research station in the High Canadian Arctic. During the past three field seasons, 1998-2000, NASA/Ames researchers have investigated the field practices of scientists and engineers at Haughton Crater on Devon Island in the Canadian Arctic, 500 miles north of the Arctic Circle, as participants in the NASA Haughton-Mars Project (HMP). The HMP is a Mars analog field research program based at SETI and Ames, with thrusts in
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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 | 0.013 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".