A Method for Lunar Surface Autonomy Certification: Application to a Construction Pathfinder Mission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Developing autonomous technologies will enable humanity to considerably expand our lunar and space exploration capabilities. Along with the technical challenges of developing autonomous technologies, there is also the issue of trust—stakeholders are often resistant to their use for a variety of psychological reasons. Nevertheless, several successful methods for gradually building trust have been developed for both terrestrial and space applications. Relevant case studies provide insights on how trust is built for stakeholders when it comes to self-driving vehicles, Artificial Intelligence in aviation, space station operations, satellite rendezvous missions, and Mars rover surface operations. Based on these case studies, we propose a generalized method for building trust with stakeholders and have applied it to a lunar construction pathfinder mission currently in development. Metrics for assessing success criteria for autonomous systems are provided as a means to progress through the proposed phases of autonomy deployment.
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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) | 0.001 | 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 it