A Behavioral Probabilistic Risk Assessment Framework for Managing Autonomous Underwater Vehicle Deployments
Bibliographic record
Abstract
Abstract The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".