Coherent structures beneath wind waves and their influence on air‐water gas transfer
Bibliographic record
Abstract
Coherent structures generated beneath laboratory wind waves were investigated using particle image velocimetry. An algorithm was developed to detect these structures and to determine their size, vorticity, and kinetic energy. As the wind speed increased from 4.5 to 11.0 m s−1, the maximum vorticity of the coherent structures increased by 40%, their average size increased by 20%, their frequency of occurrence increased 400%, and the fraction of the water surface renewed by coherent structures increased from 0.12 to 0.33. Distributions of the total kinetic energy of the coherent structures as a function of size showed that the most energetic eddies occurred in the size range 0.8–1.6 cm in diameter. The near‐surface flow could be divided into areas with one of two distinct characteristics: energetic regions occupied by coherent structures and quiescent regions largely devoid of coherent structures. A surface renewal model for air‐water exchange was used to calculate the local transfer velocity in both types of regions. The model predicted that the gas transfer velocities in the energetic regions were 2.8 times larger than in the quiescent regions and that 60% of the total air‐water gas flux occurred across the energetic regions at all wind speeds. In addition, the rate of turbulent kinetic energy dissipation was ∼2.5 times higher in the energetic regions compared to the quiescent regions at all wind speeds.
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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 | 0.000 | 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".