A probabilistic seabed-ice keel interaction model
Bibliographic record
Abstract
Abstract. In some coastal regions of the Arctic Ocean, as well as in shallow seasonally ice-covered seas, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. Recently, a grounding scheme representing this effect on sea ice dynamics was introduced and tested in a coupled ice-ocean model. This grounding scheme, based on a parameterization of ridged keel thickness linearly correlated to the mean thickness, improves the simulation of landfast ice in many regions such as in the East Siberian Sea, the Laptev Sea and along the coast of Alaska. Nevertheless, this parameterization is based solely on the mean properties of sea ice. Here, we extend the parameterization by taking into account subgridscale ice thickness distribution and bathymetry distributions, which are generally non-normal, and by computing the maximum seabed stress as a joint probability interaction between the 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 in 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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".