A model study of convergent dynamics in the marginal ice zone
Bibliographic record
Abstract
With the increasing resolution of operational forecasting models, the marginal ice zone (MIZ), the area where waves and sea ice interact, can now be better represented. However, the proper mechanics of wave propagation and attenuation in ice, and especially their influence on sea ice dynamics, still remain poorly understood and constrained in models. Observations have shown exponential wave energy decrease with distance in sea ice, particularly strong at higher frequencies. Some of this energy is transferred to the ice, breaking it into smaller floes and weakening it, as well as exerting a stress on the ice similar to winds and currents. In this article, we present a one-dimensional, fully integrated wave and ice model that has been developed to test different parameterizations of wave-ice interactions. The response of the ice cover to the wind and wave radiative stresses is investigated for a variety of wind, wave and ice conditions at different scales. Results of sensitivity analyses reveal the complex interplay between wave attenuation and rheological parameters and suggest that the compressive strength of the MIZ may be better represented by a Mohr-Coulomb parameterization with a nonlinear dependence on thickness. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".