Matching of coastal and open ocean wave models in a Mesoscale application over Lake Erie
Bibliographic record
Abstract
Three widely used wave models, namely, the open ocean wave model (Cycle‐4.5, hereinafter referred to as WAM4.5) and the coastal models, Simulation of WAves Nearshore (Cycle III version 40.31, hereinafter referred to as SWAN) and the K‐model, are applied to Lake Erie to simulate waves at a spatial resolution of about 4 km. The results of a three‐week hindcast study are compared with buoy observations in terms of integrated parameters, one‐dimensional (1‐D) and two‐dimensional (2‐D) energy spectra, scatter plots and statistical analyses of the wave fields. The time development of the 1‐D spectra by the models matches the buoy measurements well. All the wave models tend to overpredict the wave heights and underpredict (particularly the K‐model) the peak period. SWAN performs best for the wave heights and WAM4.5 for the peak periods and is computationally less demanding, whereas the spatial resolution applied to Lake Erie seems to be too coarse for an adequate use of the K‐model. In general, WAM4.5 has advantages over coastal wave models in operational intermediate‐scale applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".