Numerical Study on Tidally Induced Cross-Frontal Mean Circulation
Bibliographic record
Abstract
An analytical solution for a circulation across a tidal front obtained by Dong, C., Ou, H-W., Chen, D., & Visbeck, M. (2004). Tidally induced cross-frontal mean circulation: Analytical study. Journal of Physical Oceanograph, 34, 293–305) suggests that the cross-frontal circulation can be decomposed into four parts: frontal cell, Ekman cell, Bernoulli cell, and Stokes drift. This study examines the analytical solution thoroughly using a two-dimensional numerical model solving primitive Navier-Stokes equations. The direct comparison between the numerical and the analytical models of three cases (winter tidal front, summer tidal front, and no front) with the same configurations demonstrates that the analytical solution captures the major features of the cross-frontal circulation. A series of numerical experiments are applied to study the sensitivity of the cross-frontal circulation to physical variables: tidal intensity, horizontal topography scale, frontal strength, and vertical eddy viscosity. The ratio of the tidal excursion distance to the topographical scale is crucial to the Ekman cell in the homogenous ocean. With a greater density gradient, both the Bernoulli and Ekman cells are enhanced. Assumptions made in the analytical model are also examined. The uniform eddy viscosity and linearization in the analytical model could overestimate the bottom flow in the Ekman cell and underestimate the Bernoulli cell in the shallower region, respectively. The influence of the internal tide on the cross-frontal circulation is discussed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".