Development of Ice-Load Algorithm for Real-Time Feedback During Simulator Training
Bibliographic record
Abstract
Abstract The use of simulator training for ice navigation is becoming increasingly widespread, in part due to the difficulty, risk, and expense of providing training on vessels in ice-covered waters. Simulator training offers the opportunity for comparatively inexpensive and low risk training for novices in ice-covered conditions. For effective ship operation, it is important to use a vessel to its capacity in ice, without exceeding this capacity and causing damage to the structure. To this end, an algorithm was developed that detects ship-ice collisions, calculates the ice-loads on the structure, and provides the operator with feedback in terms of how close the load is to exceeding the safe capacity of the structure. At present, the algorithm is ship specific in that its development uses finite element analysis for a specific ship to assess the ship’s structural response for a given ice thickness, strength, and contact geometry, to determine the maximum energy at which a safe collision can occur. This safe collision energy limit can then be implemented into simulator training to give real-time feedback to participants about the safety of ship-ice collisions as they occur. An experimental campaign to test the effect of the feedback for simulator training, as well as its utility as an aid onboard the bridge of ships, is proposed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".