The Vertical Structure of the Wave Bottom Boundary Layer over a Sloping Bed: Theory and Field Measurements
Bibliographic record
Abstract
Theoretical solutions for the wave bottom boundary layer (WBL) over a sloping bed are compared with field measurements in the nearshore zone.The WBL theory is constructed using both viscoelastic-diffusion and conventional eddy viscosity turbulent closure models.The velocity solutions are then matched with those of the interior flow, given by Chu and Mei potential theory for surface gravity waves over a sloping bottom.The field measurements were obtained with a coherent Doppler profiler over a 2Њ bed slope.Results are presented for both flat and rippled bed conditions, the latter being characterized by low steepness, linear transition ripples.Close to the bed, the observed velocity profiles change rapidly in amplitude and phase relative to potential flow theory, indicating the presence of a wave boundary layer with a thickness of 3-6 cm.The observed velocity and shear stress profiles are in good agreement with the theory.The sloping bottom has significant effects on the vertical velocity, but not on the horizontal velocity and shear stress.Bottom roughness and friction velocity are estimated from optimizing the model-data comparisons.The friction velocities and wave friction factors are found to be consistent with values obtained from the momentum integral method and from the nearbed turbulence intensity, and with Tolman's semiempirical formulation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".