Risk Analysis for Vessel Accident Prevention in Marine Areas: An Accident-Theoretic Perspective on Spatial Aspects of Risk
Bibliographic record
Abstract
Abstract Area-based marine management approaches aim to mitigate the risks and impacts of shipping on human safety at sea and on ecosystems in marine and coastal environments. Through various regulatory initiatives and policy practices, risk assessment has been established as an important element to support decision-making for area-based marine management. This chapter focuses on the use of risk assessment for supporting decisions to manage navigational risks through risk control measures such as the design of vessel traffic separation schemes, the selection and positioning of aids to navigation, and the definition of operational requirements from a vessel traffic management perspective. To facilitate further developments in this domain, this chapter provides a brief overview of risk analysis techniques currently promoted at the international level, and selected approaches proposed in the academic literature are outlined. A discussion is provided on these selected techniques, through the lens of accident causation theories, focusing on how aspects related to the marine space are conceptualized in these techniques. Finally, directions for future research and development are outlined.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".