Toward a method for downscaling sea ice pressure
Bibliographic record
Abstract
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 km) and the typical dimensions of ships (a few tens of m) navigating in ice-covered regions. In this paper, we investigate the downscaling of sea ice pressure from the km-scale to scales relevant for ships. Results show that sub-grid scale pressure values can be significantly larger than the large-scale pressure (up to $\\sim$ 4x 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 that a ship creates its own high stress concentration by forming a lead in its wake while navigating. These results also highlight the difficulty of forecasting the small-scale distribution of pressure and especially the largest values. Indeed, this distribution strongly depends on variables that are not well constrained: the rheology parameters and the small-scale structure of sea ice thickness (more importantly the length of the lead behind the ship).
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".