Working toward improved small‐scale sea ice‐ocean modeling in the Arctic seas
Bibliographic record
Abstract
Until recently the main motivation in sea ice modeling has been toward the development of large‐scale models for climate studies. These models describe sea ice as a plastic material, with a smooth yield surface and ice strength dependent on a thickness distribution that is based on statistical representations of sea ice deformation through ridging. With tuning, they are found to reproduce ice extent and concentration in the Arctic and Antarctic, though velocity fields are overly smooth and many details, such as polynyas and leads, are not captured. There is increasing interest in regional ice modeling. In the near‐shore Beaufort and Chukchi seas, there is considerable interest from the oil industry in the formation and breakup of landfast ice, the propagation of oil spills, and prediction of sea ice conditions. The importance of resolving eddies in the ocean and modeling small‐scale (sub‐10‐km) sea ice processes is becoming apparent, as we begin to understand the non‐linear effect of small‐scale processes on the large‐scale motion. Recently, there have been advances in the direction of small‐scale process research and regional ice‐ocean model development. The most pertinent of these are outlined in this article.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".