Investigations on Bore Capture Modulation on a Macrotidal Beach
Bibliographic record
Abstract
Abstract The ability to empirically and accurately predict runup on natural beaches is made difficult by the random nature of waves and extreme runup events. In some cases, extreme runup events are the result of bore capture where one broken wave passes over the front of and merges with another broken wave or shoreline capture where capture occurs at the instantaneous shoreline. Here we use high resolution Lidar data to investigate potential drivers of bore and shoreline capture on a macro‐tidal dissipative beach. The proportion of runup events that are derived from capture(s) was identified within normalized runup elevation percentiles which increased from 15% in the lowest tenth percentile of runup elevations to 55% of runup events in the highest tenth percentile. Bore capture was found to occur primarily on the rising infragravity wave in both space and time, whereas shoreline capture occurred predominately during the rising and peak phases of the infragravity wave. The occurrence of bore capture was not, however, fully restricted to particular infragravity phases, suggesting multiple drivers of capture. Bore trajectories of pairs of captured bores and non‐captured bores were tracked showing that the probability of capture is also a function of normalized interwave proximity, the ratio of depths beneath consecutive wave crests, and normalized proximity to the mean shoreline. More dissipative beaches therefore not only have more infragravity energy within the surf and swash zones (thus infragravity modulation of bore capture), the wider surf zones provides greater time for bores to capture preceding bores.
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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 | 0.000 |
| 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) | 0.001 | 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".