INVESTIGATIONS OF BORE-BORE CAPTURE ON A MACROTIDAL BEACH
Bibliographic record
Abstract
The aim of better understanding the mechanisms of extreme runup events has led to increasing interest into bore-bore capture statistics and drivers. Bore-bore capture occurs when a broken wave (bore) travels over the front of another broken wave on approach to the shore. A similar but distinct process is shoreline capture which is where a broken wave travels over an uprush swash lens (therefore located in the swash zone), Bore-bore capture events occur in the surf and outer swash zones and have been shown to greatly influence runup statistics on natural beaches (Stringari and Power, 2020). Garcia-Medina et al. (2017) investigated bore-bore capture on a dissipative beach using numerical modelling and found that bore-bore capture was correlated to the largest runup events. Stringari and Power (2020) expanded on this by investigating bore-bore capture on 7 different beaches and found that bore-bore capture was responsible for over 97 percent of extreme shoreline maximas. The exact mechanisms behind bore-bore capture which result in extreme runup events in the form of energy transfer, however, are yet to be investigated. Whilst the relationship between infragravity energy at the shoreline and the probability of bore capture has been identified (Stringari and Power, 2020), the influence of infragravity energy on runup elevations resulting from captured and non-captured waves is yet to be fully quantified.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".