Working toward improved small‐scale sea ice‐ocean modeling in the Arctic seas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it