A probabilistic seabed–ice keel interaction model
Bibliographic record
Abstract
Landfast ice is a common coastal feature in the Arctic Ocean and around the Antarctic continent. One contributing and stabilizing mechanism is the grounding of sea ice ridges in shallow water. Recently, a grounding scheme representing this effect on sea ice dynamics was developed in order to improve the simulation of landfast ice by continuum-based sea ice models. This parameterization assumes that the ridged keel thickness is proportional to the mean thickness. Results demonstrated that this simple parameterization notably improves the simulation of landfast ice in many regions such as in the East Siberian Sea, the Laptev Sea and along the Alaskan coast. Nevertheless, a weakness of this approach is that it is based solely on the mean properties of sea ice. Here, we extend the parameterization by taking into account subgrid-scale ice thickness distribution and bathymetry distribution, which are generally non-normal, and by computing the maximum seabed stress as a joint probability interaction between the sea ice and the seabed. The probabilistic approach shows a reasonably good agreement with observations and with the previously proposed grounding scheme while potentially offering more physical insights into the formation of landfast ice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".