Observations of whitecap coverage and the relation to wind stress, wave slope, and turbulent dissipation
Bibliographic record
Abstract
Abstract Shipboard measurements of whitecap coverage are presented from two cruises in the North Pacific, and compared with in situ measurements of wind speed and friction velocity, average wave steepness, and near‐surface turbulent dissipation. A threshold power law fit is proposed for all variables, which incorporates the flexibility of a power law with the threshold behavior commonly seen in whitecapping. The fit of whitecap coverage to wind speed, U10, closely matches similar relations from three recent studies, particularly in the range of 6–14 m/s. At higher wind speeds, the whitecap coverage data level off relative to the fits, and an analysis of the residuals shows some evidence of reduced whitecapping in rapidly developing waves. Wave slope variables are examined for potential improvement over wind speed parameterizations. Of these variables, the mean square slope of the equilibrium range waves has the best statistics, which are further improved after normalizing by the directional spread and frequency bandwidth. Finally, the whitecap coverage is compared to measurements of turbulent dissipation. Though still statistically significant, the correlation is worse than the wind or wave relations, and residuals show a strong negative trend with wave age. This may be due to an increased influence of microbreaking in older wind seas.
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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 | 0.001 |
| 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) | 0.001 | 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".