Parameterizing Surface and Internal Tide Scattering and Breaking on Supercritical Topography: The One- and Two-Ridge Cases
Bibliographic record
Abstract
Abstract A parameterization is presented for turbulence dissipation due to internal tides generated at and impinging upon topography steep enough to be “supercritical” with respect to the tide. The parameterization requires knowledge of the topography, stratification, and the remote forcing—either barotropic or baroclinic. Internal modes that are arrested at the crest of the topography are assumed to dissipate, and faster modes assumed to propagate away. The energy flux into each mode is predicted using a knife-edge topography that allows linear numerical solutions. The parameterization is tested using high-resolution two-dimensional numerical models of barotropic and internal tides impinging on an isolated ridge, and for the generation problem on a two-ridge system. The recipe is seen to work well compared to numerical simulations of isolated ridges, so long as the ridge has a slope steeper than twice the critical steepness. For less steeply sloped ridges, near-critical generation becomes more dominant. For the two-ridge case, the recipe works well when compared to numerical model runs with very thin ridges. However, as the ridges are widened, even by a small amount, the recipe does poorly in an unspecified manner because the linear response at high modes becomes compromised as it interacts with the slopes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".