Friction of sea ice on various construction materials
Bibliographic record
Abstract
A series of tests was performed at the Canadian Hydraulics Centre to investigate friction between sea ice and various materials such as concrete, steel, wood and ice. The tests examined the effects of the change in the friction coefficient corresponding with the deterioration of material surface, speed, temperature, surface wetness and normal pressure. A carriage translated an ice specimen back and forth relative to samples of various construction materials fixed to the tank floor, while measuring the normal and tangential forces between the ice and the sample surface. Results from the test series indicated that friction was higher at lower speeds and also on rough materials. There was a great deal of variability observed in the instantaneous values of the coefficient of friction. Temperature had a weak effect on the friction coefficient, with slightly higher values of friction at higher temperatures, and there was a weak trend of lower friction with higher contact pressures. The average coefficient of friction of sea ice on smooth concrete, painted steel and sea ice was about 0.05 for speeds greater than 5 cm/s and increased to about 0.1 at 1 cm/s. The average coefficient of friction of sea ice on rough concrete and corroded steel was about 0.1 at speeds greater than 10 cm/s and increased to 0.2 at 1 cm/s.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".