Bibliographic record
Abstract
Wave friction factors are estimated from vertical profiles of near-bed turbulence and horizontal velocity spanning the wave bottom boundary layer. Measured values are partitioned by bed state, which ranged from irregular ripples to flat bed, and are examined as a function of two traditionally selected parameters—physical bed roughness and outer flow Reynolds number. The measurements are from two field experiments in very different nearshore environments: a relatively protected unbarred pocket beach and a linear barred beach exposed to the open shelf (Duck). Measured wave friction factors are remarkably similar for the two beaches and are highest for low-energy rippled beds and lowest for the high-energy flat bed conditions. The reduction in the friction factor for high-energy conditions corresponds to a decrease in the physical roughness of the bed as wave energy increases. As a function of relative roughness, measured friction factors are generally consistent with previous laboratory results and theoretical results for the high-energy cases, but the predicted values for the low-energy rippled beds show some significant differences. A new expression is derived for the bed roughness and is found to have a stronger dependence on ripple steepness than previously suggested laboratory-based relationships. Estimated friction factors exhibit a power-law dependence on Reynolds number and occupy a narrow band within the rough turbulent and transition regions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".