Bayesian Model of Operator Challenges in Maritime Pilotage
Bibliographic record
Abstract
Maritime pilotage operations are required for many vessels in their early and final parts of their voyages. The pilotage operations are meant to increase safety and efficiency for sailing in challenging areas which local maritime pilots have expert knowledge of. Regardless, several accidents involving pilotage operations have occurred (e.g. Federal Kivalina, Godafoss, and Crete Cement accidents), suggesting necessary further research and understanding of the full extent of the associated operational challenges. In the current study, we empirically investigated and identified 15 challenges associated with a Norwegian pilotage operation using interviews and content analysis. The 15 challenges are quantified and integrated into a conceptual Bayesian Network model for helping to identify the probability of the crew to face complications during the pilotage. This modeling of navigational challenges in maritime operations can infer and pinpoint areas of improvement. Finally, we discuss the results and implications.
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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 | 0.000 | 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) | 0.000 | 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".