Examinations of Surface Waves and Ocean Circulation During Severe Weather Conditions over the Northwest Atlantic Using a Coupled Wave–Current Modelling System: An Overview
Bibliographic record
Abstract
Upper oceans are highly energetic during a severe weather event, with large surface waves and intense ocean currents over areas affected by the storm. An advanced and coupled wave–current modelling system for the northwestern Atlantic (WCMS-NWA) was developed by the Regional Modelling Group at Dalhousie University for simulating surface waves and three-dimensional ocean currents over the eastern Canadian shelf and adjacent waters. Applications of this modelling system during different weather events have been discussed separately in the past. In this overview, the model performances of WCMS-NWA and major hydrodynamics over the study region during 11 severe storms are summarized. Comparisons of model results with in situ oceanographic observations and satellite data demonstrate the suitability and accuracy of WCMS-NWA in simulating ocean circulation and surface waves during normal weather conditions and intense ocean currents and extreme surface waves during severe storm events. It is shown that satisfactory model performance of WCMS-NWA requires reliable temporal–spatial representations of atmospheric forcing and inclusions of interactions between waves and currents (WCIs). Important contributions from WCIs during these severe weather conditions are discussed based on the model results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".