Toward a method for downscaling sea ice pressure for navigation purposes
Bibliographic record
Abstract
Sea ice pressure poses great risk for navigation; it can lead to ship besetting and damages. Contemporary large-scale sea ice forecasting systems can predict the evolution of sea ice pressure. There is, however, a mismatch between the spatial resolution of these systems (a few kilometres) and the typical dimensions of ships (a few tens of metres) navigating in ice-covered regions. In this paper, the downscaling of sea ice pressure from the kilometre-scale to scales relevant for ships is investigated by conducting high-resolution idealized numerical experiments with a viscous-plastic sea ice model. Results show that sub-grid-scale pressure values can be significantly larger than the large-scale pressure (up to ∼ 4 times larger in our numerical experiments). High pressure at the sub-grid scale is associated with the presence of defects (e.g. a lead). Numerical experiments show significant stress concentration on both sides of a ship beset in sea ice, especially at the back. The magnitude of the stress concentration increases with the length of the lead (or channel) behind the ship and decreases as sea ice consolidates by either thermodynamical growth or mechanical closing. These results also highlight the difficulty of forecasting, for navigation applications, the small-scale distribution of pressure, and especially the largest values as the important parameters (i.e. the length of the lead behind the ship and the thickness of the refrozen ice) are not well constrained.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".