Large-Scale Hull Loading of Sea Ice, Lake Ice, and Ice in Tuktoyaktuk Harbour
Bibliographic record
Abstract
As part of an INSROP project, large-scale hull loading of first-year sea ice, two series of experiments were carried out to simulate ice loading on a ship’s hull. The first, Phase I, was a preliminary series on freshwater lake ice near Calgary, Alberta, and the second, Phase II, took place in Tuktoyaktuk Harbour in the Canadian Arctic also on essentially freshwater ice. Loading was generated by hydraulic actuators impressing a rigid indentor against an ice edge, and by using flatjacks. A finite element analysis of the test geometry was carried out to assess the deformation and stress distributions in the ice edge for cases with both undamaged and varying degrees of damage. The calculated and measured stiffness of the ice edge agreed for a realistic selection of elastic modulus of the parent ice and damaged ice. The field results did not show conclusively any influence of damage on the failure strength of the ice. A review of these results, and those from Resolute Bay sea ice obtained earlier, showed that the nature of the ice loading, depending on whether it was uniform pressure or uniform deformation, significantly affected the results. The failure stress for uniform pressure tests did not have any dependence on area or aspect ratio. The measured field results gave average ice pressures less than those recommended by the Arctic Pollution Prevention Regulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".