Evaluation of Structured and Unstructured Models for Application in Operational Ocean Forecasting in Nearshore Waters
Bibliographic record
Abstract
The oceanography sub-initiative of Canada’s Oceans Protection Plan was tasked to develop high-resolution nearshore ocean models for enhanced marine safety and emergency response, fitting into the multi-scale, multi-level nested operational ocean forecasting systems. For decision making on eventual 24/7 operational support, two ocean models (a structured grid model, NEMO (Nucleus for European Modelling of the Ocean); and an unstructured grid model, FVCOM (Finite Volume Coastal Ocean Model), were evaluated. The evaluation process includes the selection of the study area, the requirements for model setup, and the evaluation metrics. The chosen study area, Saint John Harbour in the Bay of Fundy, features strong tides, significant river runoff and a narrow tidal-river channel. Both models were configured with the same sources of bathymetry and forcing data. FVCOM achieved 50-100 m horizontal resolution in the inner harbour and included wetting/drying. NEMO achieved 100 m resolution in the harbour with a three-level one-way nesting configuration. Statistical metrics showed that one-year simulations with both models achieved comparable accuracies against the observed tidal and non-tidal water levels and currents, temperature and salinity, and the trajectories of surface drifters, but the computational cost of FVCOM was significantly less than that of NEMO.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".