A systems-theoretic approach using association rule mining and predictive Bayesian trend analysis to identify patterns in maritime accident causes
Bibliographic record
Abstract
Accident investigations are commonly conducted to improve safety in ship design and operations. Given the lack of comprehensive approaches to understand causal factors of maritime accidents considering systems-theoretic views on accident causation, this paper presents a novel approach using information from accident investigation reports to this effect. The proposed approach combines key elements of the Causal Analysis based on Systems Theory method, Association Rule Mining and predictive Bayesian trend analysis to gain deeper understanding of patterns and trends in accident causal factors. This new approach goes beyond the state of the art by offering insights on accident causal patterns and trends at the system level, which can be used by maritime authorities and industries to enhance maritime safety by understanding co-occurring accident causes. Additionally, the approach is applied to 30 years of Canadian shipping accident reports from the Transportation Safety Board, producing new knowledge about accident causes across different commercial vessel types and accident categories. The results highlight accident causes in interactions between shipping management and vessels, and between ship crews and bridge equipment. Differences between passenger and cargo vessels, and between onboard fires and navigational accidents are observed. Discussions on results, limitations, and future research directions conclude the article.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".