Estimating Probabilistic Iceberg Design Loads on Ships Navigating in Ice Covered Waters
Bibliographic record
Abstract
Marine operations in winter and early spring, on the East Coast of Canada, offshore Newfoundland and Labrador, are affected by the presence of sea ice and icebergs. Groups such as the Canadian Ice Service (CIS), International Ice Patrol (IIP) and PAL Environmental Services monitor and track ice movement to advise operators in the region of the risk of ice encounters. As an example, ice bulletins are produced to alert mariners of the number of icebergs one may expect to encounter while navigating in a particular region. Because of the risk of encounter with ice features, the design of vessels navigating in these waters must include an appropriate level of reinforcement, particularly in the bow, to withstand impact loads. Local and global loads may be estimated using probabilistic methods based on the encounter frequency and probability of a load given an impact. Further to this approach a methodology has been developed for estimating iceberg design loads along vessel routes in ice prone regions off the East Coast of Canada. Design loads are estimated for shuttle tankers navigating along example routes from the Grand Banks to market. Loads consider encounter rates along the route and detection and avoidance strategies. Results illustrate a significant reduction in risk and resultant loads if tactical avoidance strategies are incorporated into the design. This design methodology can be applied to other arctic regions where ice types include multi-year and ridged ice and where detection and avoidance can be used to reduce the encounter frequency and hence design loads.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".