High‐angle wave instability and emergent shoreline shapes: 2. Wave climate analysis and comparisons to nature
Bibliographic record
Abstract
Recent research has revealed that the plan view evolution of a coast due to gradients in alongshore sediment transport is highly dependant upon the angles at which waves approach the shore, giving rise to an instability in shoreline shape that can generate different types of naturally occurring coastal landforms, including capes, flying spits, and alongshore sand waves. This instability merely requires that alongshore sediment flux is maximized for a given deepwater wave angle, a maximum that occurs between 35° and 50° for several common alongshore sediment transport formulae. Here we introduce metrics that sum over records of wave data to quantify the long‐term stability of wave climates and to investigate how wave climates change along a coast. For Long Point, a flying spit on the north shore of Lake Erie, Canada, wave climate metrics suggest that unstable waves have shaped the spit and, furthermore, that smaller‐scale alongshore sand waves occur along the spit at the same locations where the wave climate becomes unstable. A shoreline aligned along the trend of the Carolina Capes, United States, would be dominated by high‐angle waves; numerical simulations driven by a comparable wave climate develop a similarly shaped cuspate coast. Local wave climates along these simulated capes and the Carolina Capes show similar trends: Shoreline reorientation and shadowing from neighboring capes causes most of the coast to experience locally stable wave climates despite regional instability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".