Wave Breaking Dissipation in a Young Wind Sea
Bibliographic record
Abstract
Abstract Coupled in situ and remote sensing measurements of young, strongly forced wind waves are applied to assess the role of breaking in an evolving wave field. In situ measurements of turbulent energy dissipation from wave-following Surface Wave Instrument Float with Tracking (SWIFT) drifters and a tethered acoustic Doppler sonar system are consistent with wave evolution and wind input (as estimated using the radiative transfer equation). The Phillips breaking crest distribution Λ(c) is calculated using stabilized shipboard video recordings and the Fourier-based method of Thomson and Jessup, with minor modifications. The resulting Λ(c) are unimodal distributions centered around half of the phase speed of the dominant waves, consistent with several recent studies. Breaking rates from Λ(c) increase with slope, similar to in situ dissipation. However, comparison of the breaking rate estimates from the shipboard video recordings with the SWIFT video recordings show that the breaking rate is likely underestimated in the shipboard video when wave conditions are calmer and breaking crests are small. The breaking strength parameter b is calculated by comparison of the fifth moment of Λ(c) with the measured dissipation rates. Neglecting recordings with inconsistent breaking rates, the resulting b data do not display any clear trends and are in the range of other reported values. The Λ(c) distributions are compared with the Phillips equilibrium range prediction and previous laboratory and field studies, leading to the identification of several inconsistencies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".