Detection and Characterization of Microscale Breaking Waves
Bibliographic record
Abstract
Microscale breaking waves are short wind-generated waves that break without air entrainment. At low to moderate wind speeds microscale breaking waves play an important role in enhancing air-water heat and gas transfer. We report on a series of experiments conducted in a wind-wave flume at Harris Hydraulics Laboratory (University of Washington, Seattle) designed to investigate the importance of microscale breaking waves in generating near-surface turbulence and in enhancing air-sea gas and heat transfer rates. Non-invasive experiments were performed at wind speeds ranging from 4.5 m/s to 11 m/s and at a fetch of 5.5 m. The skin-layer or water surface temperature was measured using an infrared (IR) imager and digital particle image velocimetry (DPIV) was used to obtain simultaneous measurements of the two-dimensional velocities immediately below the water surface. Analysis of the simultaneous DPIV and infrared datasets revealed that microscale breaking waves generate strong vortices in their crests that disrupt the cool skin layer at the water surface and create thermal wakes that are visible in the infrared images. While non-breaking waves do not generate strong vortices and hence do not disrupt the skin layer. We developed a scheme based on the magnitude of vorticity in the wave crest to identify microscale breaking waves. The results show that at a wind speed of 4.5 m/s, 11% of the waves broke. The percentage of breaking waves increased with wind speed and at a wind speed of 11 m/s, 91% of the waves were microscale breaking waves. Comparison of different geometric and flow properties of microscale breaking and non-breaking waves revealed that microscale breaking waves are steeper, larger in amplitude and generate more turbulent kinetic energy compared to non-breaking waves.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".