Modelling beach-structure interaction using a Heaviside technique: application and validation
Bibliographic record
Abstract
Valsamidis, A., Cai, Y., Reeve, D.E., 2013. Modelling beach-structure interaction using a Heaviside technique: application and validationIn this study, an analytical solution, based on a Heaviside technique, is developed to model the shoreline evolution in the vicinity of a groyne due to a random sequence of waves. The beach at Borth, Wales, UK was used as a case-study. A wave time-series covering a time period of about 12 years, was used to test the performance of a recently constructed coastal defence scheme. Transformations of the wave time-series from offshore to nearshore were performed using a semi-empirical procedure. Three different wave breaking formulae were independently applied to the wave model, and their effects to the consequent shoreline evolution were investigated. In addition, three different longshore transport formulae were compared. These were the CERC, the Kamphuis and the Bayram formulae. Results showed that the CERC formula predicted a significantly greater amount of sediment transport and hence erosion on the downdrift side of the groyne while the models based on Kamphuis and the Bayram formulae gave comparable results. All the results exhibited a strong sensitivity to the temporal resolution of the forcing. Finally, some sensitivity to the treatment of wave breaking was found.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".