Numerical simulation of ice dynamics on the St. Lawrence River at Montréal
Bibliographic record
Abstract
A numerical model originally developed at the National Research Council to simulate the dynamics of sea ice over large domains has been extended and applied to simulate ice cover dynamics in the St. Lawrence River at Montréal. The model predicts the evolution of ice cover and provides estimates of ice concentration, ice thickness and internal pressure or stress over space and time subject to forcing by water currents and winds. For this application the model was setup to resolve floating ice dynamics at much higher spatial resolutions and finer time scales than before, and a new boundary condition was developed to support a continuous inflow of ice across the upstream boundary. A new 2D hydrodynamic model of flows in the St. Lawrence River was also developed to provide high-quality spatially-variable predictions of water currents in the region of interest. The new models have been applied to predict the evolution of the ice cover on the river near downtown Montréal over a 9-12 hour period for several combinations of initial ice condition, river discharge and wind representing typical conditions during spring break-up. Simulations have been carried out to provide estimates of river ice dynamics and downstream ice conditions. This paper provides an overview of the methodologies employed in the study and a summary of the key findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".