Ice Compression Risks to Shipping Over Canadian Arctic and Sub-Arctic Zones
Bibliographic record
Abstract
Abstract The paper examines ice compression (or pressure) build-up, which may pose a threat to navigation, over various zones of the Canadian Arctic and sub-Arctic waters. The results were obtained from a multi-year program conducted at the National Research Council of Canada. The objective of that program is to quantify the risks to navigation posed by compressive ice, and to develop predictive tools to aid shipping operations in an ice environment. The work consisted of a number of activities, including: the development of an ice dynamics model tailored for high-resolution simulations of ice cover drift and deformation; creating a database of besetting events and analysis of the conditions that influence the risk of besetting; hindcasting of ice and other environmental conditions that led to besetting over various geographic regions; and the development of forecasting tools to support offshore and shipping operations and training mariners. The present paper documents the governing equations of the ice dynamics and ridging model and discusses the high-resolution implementation that makes it possible to predict the risk of vessel besetting. A case of ice compression that took place in the southern Beaufort Sea is examined. The critical values of ice pressure and ridge thickness that posed a threat to vessels during that event were found compatible with estimates corresponding to besetting events in the Gulf of St. Lawrence and Frobisher Bay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".