MODEL FOR PREDICTING BEACH CHANGES USING CELLULAR AUTOMATON METHOD
Bibliographic record
Abstract
Sand deposition on the gently-sloping revetment, the slope of which is steeper than the equilibrium slope of sand, is often observed when storm waves ran up the beach. Serizawa et al. (2006) have developed the BG model, in which the cross-shore sand transport depends on the balance between the equilibrium slope of sand and the local slope of the beach, and seaward sand transport will occur when the local slope of the beach or the structure is larger than the equilibrium slope. This implies that shoreward sand transport on the slope steeper than the equilibrium slope of sand cannot be predicted by the BG model. This is because the fundamental equation of the BG model is expressed by the net sand transport defined by the sum of the sand transport under the ongoing and outgoing waves. In this study, sand transport under the ongoing and outgoing waves is independently taken into account, and a new model for predicting beach changes is developed using the cellular automaton method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".