Modelling of ice pressure build-up in the Strait of Belle Isle and Northeast Coast of Newfoundland
Bibliographic record
Abstract
Transport Canada funded a project with the objective of providing real-time information to ships operating in the Arctic to minimize safety and operational problems due to pressured ice conditions. This will be done by providing real-time information and an onboard predictive system to ship operators. A tool capable of predicting formation of ice ridges, rafting, leads opening, and ice pressure build up along the shipping routes is needed to provide such information. Canadian Hydraulics Centre of National Research Council of Canada (NRC-CHC) in collaboration with Canadian Ice Service (CIS) of Environment Canada developed an ice forecasting model. NRC-CHC has collaborated with CIS and McGill University on the development of formulations of ice properties, ice thickness distribution and forecasting. The model is capable of predicting ice drift, ice thickness redistribution, opening of leads and pressure build up on a small scale applicable to vessel navigation. The focus of this report is on comparing the model predictions with ice pressure build-up in regions where vessels were trapped in pressured ice at the Northeast Coast of Newfoundland in April 2007 and in Strait of Belle Isle in January 2008. The results of numerical simulations showed that the ice forecasting model effectively simulated the process of ice pressure build-up, ice thickness and ice concentration evolutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".