First-year Ice Conditions for Operations of the Canadian Coast Guard Polar Icebreaker
Bibliographic record
Abstract
This paper describes a study of first-year ice conditions that the new Canadian Coast Guard Polar Icebreaker is likely to encounter. About 50 years of ice thickness, snow depth and air temperatures at 10 stations in the Arctic were analyzed for average and extreme values. The maximum annual ice thickness at 8 of the 10 stations was less than 2.5 m. At two stations, Eureka and Hall Beach, maximum annual thickness greater than 2.5 m was measured but only occurred about once every 10 years. Overall an ice thickness of 2.5 m is a reasonable representation of maximum first-year ice thickness. Long term trends in maximum ice thickness and decadal comparisons were assessed and showed a trend of 1 to 3 cm decrease per decade. The flexural strength of sea ice depends directly on salinity and temperature of the ice cover, which are indirectly related to ice thickness and air temperature. The flexural strength, determined from these indirect factors, was seen to decrease from March onwards, decreasing rapidly in May and June. The flexural strength in March averaged 0.7 MPa. While flexural strength decreased from March onwards, ice thickness continues to increase through to June. Ship resistance in first-year ice at slow speeds relates to ice thickness and flexural strength. Examining the two factors indicated the maximum low speed resistance would be expected in April.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".