Wave Attenuation due to Ice Cover: An Experimental Model in a Wave-Ice Flume
Bibliographic record
Abstract
Waves penetrate deep into the ice covered seas, inducing breakup of the ice cover. Concomitantly, the ice cover attenuates the wave energy over distance, so that wave impacts die out eventually. Observations of wave attenuation and concurrent wave-induced breakup in the literature are serendipitous due to difficulties in making measurements in ice covered seas. Hence understanding of wave-ice interactions remain uncertain. Here we present measurements of wave propagation through ice covered waters in the new experimental wave-ice facility at the University of Melbourne. The facility comprises of a 14m long and 0.76m wide flume in a refrigerated chamber, where temperatures can be lowered down to −12 degrees Celsius to generate a continuous ice cover on the water surface. A wave maker, installed at one end, is used to generate regular waves, ranging from gently-sloping to storm-like conditions. Wave attenuation rates are determined from video-camera images of the displacements of markers embedded in the ice cover. The experiments investigated wave propagation through the continuous ice cover, breakup, and propagation through the broken ice cover. Spatial evolution of the breakup and geometrical properties of floes are monitored and correlated with incident wave properties. Wave attenuation over broken ice is investigated and compared against the continuous ice case.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".