Arctic landfast ice simulation with brittle rheology and probabilistic grounding
Bibliographic record
Abstract
Landfast ice, i.e., sea ice that is mechanically immobilized for several weeks along the coasts, significantly influences the underlying ocean by controlling the occurrence of coastal polynyas and the formation of dense water within. However, it is usually poorly represented in numerical models. In the Arctic, the accurate simulation of landfast ice relies on parameterizing sea ice grounding in shallow water areas and on the sea ice rheology capability to form ice arches in regions with restricted geometry. In this study, we compare a brittle rheology (i.e., the Brittle Bingham-Maxwell or BBM one), newly implemented in the ocean-sea ice model NEMO-SI3, with a standard viscous-plastic rheology (i.e., the aEVP), which is widely used in sea ice models. The performance of the two rheologies in forming ice arches and landfast ice is evaluated at the scale of the Arctic at a 0.25° horizontal resolution. For the grounding parameterization, we apply a probabilistic grounding scheme based on the ice thickness distribution and investigate how leveraging subgrid-scale bathymetry statistics can enhance its performance.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".