Laboratory Observations of Frazil Ice Accumulation during Freeze-Up Stage
Bibliographic record
Abstract
A series of experiments was conducted using a laboratory flume within the University of Ottawa cold room to measure accumulation of frazil ice and ice jamming for open channel flows over various bed materials and different water depths. The water surface level was monitored using four ultrasonic devices mounted above the experimental flume, while the amount of ice accumulation was determined through image processing techniques. To implement the image processing, a deep learning semantic segmentation technique capable of identifying surface ice was employed. To calculate the volume of frazil accumulation in each experiment, the obtained surface area during the residual stage, when the water temperature stabilized slightly below the freezing point, was multiplied by the submerged ice thickness. The submerged ice thickness was calculated using a time-based polynomial function, which approximately fits the measured ice lower levels for various experiments. For rough and fully turbulent flows, no surface skim ice was observed, but frazil ice accumulated at the flume’s end. The resulting surface ice propagated upstream at a rate of 0.6 cm/min after the supercooling stage. In contrast, in tests with lower turbulence and higher water depth, a combination of frazil and border skim ice was observed, with maximum ice cover progression rates of 2 cm/min after supercooling was reached.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".