On Equilibrium Properties in Predictive Modeling of Coastal Morphology Change
Bibliographic record
Abstract
Most computation-intensive numerical models of coastal morphology change have not yet incorporated behavior related to equilibrium states. This behavior may involve limiting depths for given waves and currents, limiting slopes, and limiting volumes of morphologic features such as ebb-tidal shoals. As a result, the simulation time scale in these models is in doubt. It is unclear whether a prediction has proceeded to a reasonable physical state or to a spurious one dictated by the nonlinear processes contained in the governing equations and imprecise initial and boundary conditions. Regularity in coastal morphology and equilibrium forms from micro-scale through regional scale can guide modeling efforts. This paper discusses the benefit of incorporating equilibrium properties in coastal morphology modeling. An example illustrates how micro-scale physical-based calculations might be combined effectively with a bounded macro-scale model of tidal inlet shoal evolution and sediment bypassing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".