Investigating the Influence of Bridge Officer Experience on Ice Management Effectiveness Using a Marine Simulator Experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 it