Modeling the Dynamics of the North Water Polynya Ice Bridge
Bibliographic record
Abstract
Abstract The North Water polynya, the largest polynya in the world, forms annually and recurrently in Smith Sound in northern Baffin Bay. Its formation is governed in part by the formation of an ice bridge in the narrow channel of Nares Strait below Kane Basin. Here, the widely used elastic–viscous–plastic elliptical rheology dynamic sea ice model is applied to the region. The idealized case is tested over a range of values for e = [1.2, 2.0] and initial ice thicknesses from 0.75 to 3.5 m, using constant northerly winds over a period of 30 days, to evaluate long-term stability of different rheological parameterizations. Idealized high-resolution simulations show that the formation of a stable ice bridge is possible for e ≤ 1.8. The dependence of the solution in terms of grid discretization is studied with a domain rotated 45°. A realistic domain with realistic forcing is also tested to compare time-variant solutions to actual observations. Cohesion has a remarkable impact on if and when the ice bridge will form and fail, assessing its importance for regional and global climate modeling, but the lack of observational thickness data during polynya events prevents the authors from identifying an optimal value for e.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".