REMOTE MEASUREMENT AND PREDICTION OF BREAKING WAVE PARAMETERS
Bibliographic record
Abstract
The analysis of wave breaking in shallow water has been on-going for almost 150 years. Numerous research papers have been published that investigate methods to predict breaking conditions and the geometric characteristics of breaking waves. This study presents a novel, safe, and low cost method to extract breaking wave properties from irregular waves in the surf zone, using optical and in-situ measurement systems. Sensitivities studies on methods of measuring the breaking water depth are compared and the water depth at the wave trough depth, corrected for optical offsets using a still water correction of 1/3 wave height, is found to be exhibit the least variability. A new effective seafloor slope definition, based on individual breaking wavelength to depth ratios, was found to increase predictive ability over previously variable seafloor slope extraction methods. Collected field data is compared against established breaking wave height formulas with general exponential form consistently finding best correlation. An optimized breaking wave height predictor featured a root mean square relative error of only 1.672% against the measured dataset. Finally, the study of the geometric shape of the plunging wave vortex as a possible indicator for the breaking intensity of ocean waves has been ongoing for almost 50 years with limited success. The validity of using the vortex ratio and vortex angle as a method of predicting breaking intensity is examined. Through the first complete analysis of field collected irregular wave breaking vortex parameters it is illustrated that the vortex ratio and vortex angle cannot be accurately predicted using standard breaking wave characteristics and hence are not suggested as a possible indicator for breaking intensity
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".