Towards system-theoretic risk assessment for future ships: A framework for selecting Risk Control Options
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
While the concept of smart shipping is expected to shape the future of the maritime industry, its safety is still a major concern. New risks might emerge when shifting from human controllers onboard, to autonomous software controllers and remote human controllers. The uncertainties associated with the emerging risks require an efficient decision-making methodology to ensure ship safety. This paper proposes a framework for selecting Risk Control Options (RCOs) of ships with higher degrees of autonomy in the context of marine risk assessment and Formal Safety Assessment (FSA). The framework uses the System Theoretic Process Analysis (STPA) for the hazard analysis and the identification of RCOs, while Bayesian Network is employed in the framework for estimating the system risk. Integrating STPA and BN offers the possibility to cover most of the steps of both risk assessment and FSA and permits the prioritization of the identified RCOs. The proposed method is applied to a concept of an autonomous seawater cooling system (SWC) as an illustrative case study. The results indicate that the RCOs including sensors health monitoring and software testing should be prioritized to reduce the risk. This is unveiled by the STPA analysis which shows the risk contribution of the associated causal scenarios.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it