High-resolution modelling of a coastal harbour in the presence of strong tides and significant river runoff
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In the context of Canada’s Ocean Protection Plan (OPP), improved coastal and near-shore modelling is needed to enhance marine safety and emergency response capacity in the aquatic environment. In this study, the Nucleus for European Modelling of the Ocean (NEMO) is adopted to develop an ocean forecasting system for Saint John harbour in the Bay of Fundy, on the east coast of Canada. The challenging regional oceanography is characterized by the presence of some of the world’s strongest tides, significant river runoff and complicated geometry. A three-level one-way nesting approach is used to downscale from a 1/12° North Atlantic-Arctic regional model to very-high-resolution port-scale around Saint John harbour. The three nested grids cover the outer shelf, the Bay of Fundy and finally the approach to the harbour with resolutions of 2.5 km, 500 m and 100 m respectively. Due to the lack of accurate runoff data at the Saint John River outlet, the model’s lateral open boundary condition is modified to introduce the river forcing with the observed time series of water level near the mouth of the river. Evaluation with observational data demonstrates the model’s accuracy for the simulation of tidal elevation and currents, non-tidal water level and currents, temperature and salinity. Comparison with the observed sea surface temperature demonstrates the improved model accuracy through increasing the horizontal resolution. Virtual Lagrangian trajectories computed using the modelled surface currents and including wind effects show good agreement with the observed trajectories of different types of surface drifters. This study demonstrates the capability of the NEMO modelling framework to provide very-high-resolution modelling at port-scale resolution for the Saint John harbour.
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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) | 0.000 | 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 it