Numerical simulations of ice thickness redistribution and ice drift in the Gulf of St. Lawrence
Bibliographic record
Abstract
Canadian Hydraulics Centre of National Research Council of Canada (NRC-CHC) in collaboration with Canadian Ice Service (CIS) of Environment Canada developed an ice forecasting model. Development of this model was initiated to respond to CIS navigation requirements for shipping and navigation in Canadian Arctic. The ice conditions in Canadian Arctic Archipelago are unique due to presence of multi-year ice, ridging, rafting, formation and collapse of ice bridges in the narrow and converging channels, leads opening, and ice pressure build up. Since ice thickness redistribution due to deformation of ice cover is an essential component of high resolution ice forecasting, a parameterization of all the complex processes must be used in order to account for thickness redistribution and lead openings. The NRC-CHC ice forecasting model deals with these processes through thickness distribution model. That model is an important component of the ice forecasting model. It accounts for continuous evolution of ice thickness and concentration and for the transfer from level to ridged ice. Convergence and shear deformation of the ice cover are considered to transfer part of the level ice into ridged ice. This report presents validation of the NRC-CHC ice thickness redistribution model with field data. The results show that the field observations and model predictions are in a good agreement and that model simulations were able to predict deformation and drift of the sea ice.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".