Investigating the Influence of Bridge Officer Experience on Ice Management Effectiveness Using a Marine Simulator Experiment
Bibliographic record
Abstract
The research investigates the influence of human expertise on the effectiveness of ice management operations. Ice management is defined as a systematic operation that enables a marine operation to proceed safely in the presence of sea ice. In this study, a method has been applied for assessing the effectiveness of ice management operations in terms of ability to modify the presence of pack ice around an offshore structure. This was accomplished in a real-time marine simulator environment as the venue for a systematic investigation. In the simulator, volunteer participants from a range of seafaring experience levels were tasked with individually completing ice management tasks. Recorded from 36 individuals’ simulations was the extent to which the ice in a defined area was impacted, measured in terms of tenths ice concentration. These responses were then compared to two independent variables: 1) experience level of the participant, categorized as either cadet or seafarer, and 2) ice severity, measured in ice concentration. The results showed a significant difference in ice management effectiveness between experience categories, where effectiveness was defined as the average drop in ice concentration during simulation. Results also showed that the human factor of experience and the environmental factor of ice concentration both had significant effects on average concentration drop. The research provides insight into the relative importance of vessel operator skills in contributing to effective ice management, as well as how this relative importance changes as ice conditions vary from mild to severe. This may have implications for training in the nautical sciences and could help to inform good practices in ice management.
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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 | 0.003 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".