On estimation of coastal wave parameters and wave‐induced shear stresses
Bibliographic record
Abstract
Abstract Wave parameters, e.g., wave height, near‐bed wave orbital velocity, and wave‐induced shear stresses, are important hydrodynamic parameters for sediment processes in coastal oceans. Wave orbital velocity is particularly critical in sediment resuspension. Several algorithms to calculate wave orbital velocity have been proposed, including linear wave theory, spectrum, and Joint North Sea Wave Project methods, but the validity of these algorithms in relatively shallow waters is not well understood. In this study, we compared the wave parameters obtained by different instruments and algorithms at four sites, one within the intertidal zone with a mean depth of 1 m and the remainder three in deeper offshore water with mean depths of 15–30 m. We found a high consistency of the estimated wave height, peak wave period, and wave orbital velocity among different datasets and different algorithms at the offshore sites, while there were significant discrepancies at the shoreline site. Using Ursell number, our study suggests that it is reliable to apply any of the three algorithms and different instruments (acoustic Doppler velocimeter and buoy) in deeper water. However, for very shallow water, it is recommended to use the measured high‐frequency velocity and spectrum method to calculate wave orbital velocity, and use wave gauge instrument or zero‐crossing algorithm to obtain wave height and period information. Finally, the effect of turbulence and bedform morphology on wave‐induced shear stress is discussed: without removing the turbulence or taking onto account bedforms (e.g., ripples), the orbital velocity will be remarkably over‐estimated or under‐estimated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".