Whitecapping and wave field evolution in a coastal bay
Bibliographic record
Abstract
Evolution of the wave field in a coastal bay is investigated, by comparison between field observations and numerical simulations using a spectral wave model (Simulating WAves Nearshore (SWAN)). The simulations were conducted for the passage of an extratropical storm, during which surface elevation spectra were bimodal owing to local wind‐sea generation and swell propagation into the bay. SWAN was run in stationary and nonstationary mode for two whitecapping source term formulations. The first was developed by Komen et al. (1984) and is dependent on spectrally averaged wave steepness, and thus includes swell in the calculation of whitecapping dissipation and typically overestimates wind sea in the presence of swell. The second, proposed by van der Westhuysen et al. (2007), estimates whitecapping of wind sea locally in the wave spectrum and is not coupled to swell energy. This formulation reproduced the magnitude and shape of the observed wind‐sea spectral peak much better than the previous formulation. Whitecapping dissipation rates have been estimated from observations, using the equilibrium range theory developed by Phillips (1985), and are well correlated with both wind speed and acoustic backscatter observations. These rates agree with SWAN estimates using the spectrally local expression, and provide additional physical validation for the whitecapping source term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".