INTEGRATED ACCIDENT MODEL FOR MARINE CONVOY TRAFFIC IN ICE- COVERED WATERS
Bibliographic record
Abstract
Independent safe navigation in ice-covered water is difficult. Icebreaker assistance is required for sailing through ice- covered waters. This poses an additional risk of collision. The study proposes a modified Human Factor Analysis and Classification (HFACS) framework to identify and classify contributing risk factors during a convoy. HFACS integration with Nagel-Schrekenberg (NaSch) model considers an operator’s behaviour and links it with the occurrence of various risk factors. The study finds significant influence in risk from small changes in two new factors, viz., crew reduction and crew overload. For example, based on the sensitivity analysis, it is determined that about a 17% contribution of crew reduction and about a 24% of contribution of crew overload increase the contribution of risk taking by an amount of approximately 93% in the overall risk of accidents. The accident probabilities obtained here will be helpful in decision making concerning safe operations during a convoy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".