Surface and Interfacial Waves in a Strongly Stratified Upper Ocean
Bibliographic record
Abstract
Abstract In an idealized two-layer fluid, surface waves can generate waves at the internal interface through class-3 resonant triads in which all waves are propagating in the same direction. The triads are restricted to wavenumbers above a critical value kcrit that depends on the density ratio R between the two layers and their depths. We perform numerical simulations to analyze the evolution of a surface wave field, initially specified by a Pierson–Moskowitz-type spectrum, for R = 0.97 (representing a realistic lower a bound for oceanic stratification). At high initial steepness and peak wavenumber kp ≪ kcrit, the energy increases in the spectral tail; as a parameterization of resulting wave breaking, at each time step individual waves with a steepness greater than the limiting Stokes steepness are removed. The energy change of the surface wave field is a combination of energy transfer to the interfacial waves, spectral downshift, and wave breaking dissipation. At wavenumbers there is a net loss of energy, with the greatest dissipation at ≈1.3kp. The maximum gain occurs at ≈0.5kp. The onset of the spectral change shows a strong threshold behavior with respect to the initial wave steepness. For steep initial waves the integrated energy dissipation can reach >30% of the initial energy, and only ≈1% of the initial surface wave energy is transferred to the interfacial wave field. The spectral change could be expressed as an additional dissipation source term, and coupled ocean–wave models should include additional mixing associated with the interfacial waves and enhanced wave breaking turbulence.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".