Modelling the effect of waves, weathering and beach development on shore platform development
Bibliographic record
Abstract
Abstract A mathematical model was used to study shore platform development. Mechanical wave erosion was dependent on such variables as tidal range, wave height and period, breaker height and depth, breaker type, surf zone width and bottom roughness, submarine gradient, rock resistance and the elevational frequency of wave action within the intertidal zone. Also included were the effects of sand and pebble accumulation, cliff height and debris mobility, and downwearing associated with tidal wetting and drying. The occurrence, location and thickness of beaches often depended on initially quite minor variations in platform morphology, but owing to their abrasive or protective effect on underlying rock surfaces, they were able to produce marked differences in platform morphology. Generalizations are difficult, but the model suggests that platform gradient increases with tidal range. Platform width also increases with tidal range with slow downwearing but it decreases with fast downwearing. Platform gradient decreases and width increases with wave energy, and decreasing rock resistance and platform roughness. With low tidal range, platform gradient is generally lower and platform width greater with beaches of fine sand than with gravel, but the relationship is more variable with a high tidal range. Platform width increases and platform gradient decreases with the rate of downwearing on bare surfaces, particularly in low tidal range environments, but the pattern is less clear on beach‐covered platforms. Platforms with large amounts of beach sediment tend to be narrower and steeper than bare platform surfaces. Platform gradient increases and platform width decreases with increasing cliff height and with decreasing cliff debris mobility. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".