Formation and distribution of sea ice in the Gulf of St. Lawrence: A process‐oriented study using a coupled ocean‐ice model
Bibliographic record
Abstract
Abstract A coupled ocean‐ice model for the eastern Canadian shelf is used to examine main physical processes affecting sea ice conditions in the Gulf of St. Lawrence (GSL) and adjacent waters. The coupled model is based on NEMO and uses OPA9 as the ocean circulation component and LIM2 as the ice model. The coupled model is forced by atmospheric reanalysis fields produced by Large and Yeager (2004). The model results are used to examine the roles of thermodynamics and dynamics on sea ice distributions and patterns of ice production and melting, and the influence of ice capping on the circulation in the study region. Analysis of model results indicates that local production of sea ice is important in shallower areas over the northern and western GSL. Equatorward advection of sea ice from the St. Lawrence Estuary is affected significantly by the Gaspé Current. An index is used to quantify the relative importance of thermodynamic and dynamics of sea ice in the GSL. It is found that both thermodynamics and dynamics are important over most of the GSL, except for waters around Anticosti Island, in the southeastern Gulf, and over the eastern Scotian Shelf, where dynamics (or sea ice movements) are the most important mechanism for the presence of sea ice. The study also demonstrates that ice capping significantly reduces the strength of the winter circulation in the GSL.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".