A wave-based model for the marginal ice zone including a floe breaking parameterization
Bibliographic record
Abstract
[1] The marginal ice zone (MIZ) is the boundary between the open ocean and ice-covered seas, where sea ice is significantly affected by the onslaught of ocean waves. Waves are responsible for the breakup of ice floes and determine the extent of the MIZ and floe size distribution. When the ice cover is highly fragmented, its behavior is qualitatively different from that of pack ice with large floes. Therefore, it is important to incorporate wave-ice interactions into sea ice–ocean models. In order to achieve this goal, two effects are considered: the role of sea ice as a dampener of wave energy and the wave-induced breakup of ice floes. These two processes act in concert to modify the incident wave spectrum and determine the main properties of the MIZ. A simple but novel parameterization for floe breaking is derived by considering alternatively ice as a flexible and rigid material and by using current estimates of ice critical flexural strain and strength. This parameterization is combined with a wave scattering model in a one-dimensional numerical framework to evaluate the floe size distribution and the extent of the MIZ. The model predicts a sharp transition between fragmented sea ice and the central pack, thus providing a natural definition for the MIZ. Reasonable values are found for the extent of the MIZ given realistic initial and boundary conditions. The numerical setting is commensurate with typical ice-ocean models, with the future implementation into two-dimensional sea ice models in mind.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".