Performance of Nowcast and Forecast Wave Models for Lunenburg Bay, Nova Scotia
Bibliographic record
Abstract
The results from a numerical modelling system are presented for wave prediction inside Lunenburg Bay. The Bay, typical of the coast in Atlantic Canada, is an environment where ocean swell enters only from selected directions; wind-sea dominates the wave spectrum from other directions, and shallow water physics are important. The modelling system consisted of wave models for both the present time (nowcasts) and forecasts using the Simulating Waves Nearshore (SWAN) model inside the Bay. Nowcasts (stationary computations of the wave field that ran every 30 minutes) were driven by real-time observations of the directional wave boundary conditions, winds and water levels. Forecasts (48 hourly non-stationary computations) were driven by boundary conditions from the WAVEWATCH III ocean wave model (implemented on a larger domain) and winds from the Global Environmental Multiscale (GEM) atmospheric model. The results were compared with wave observations inside the Bay and provided in real-time. Model performance was assessed for a storm event with 2.8 m significant wave heights that occurred in October 2007, by comparing nowcast predictions, forecast predictions and observations. The nowcasts provided the best correlation, R2 = 0.75, with observations inside the Bay, since they were driven by observations made at the model boundary. The forecasts tended to underpredict the significant wave height and peak period, but overall the model results compared well with the data over a wide range of wind and wave conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".