Preliminary results from a two-dimensional model of wave-ice interactions in the Fram Strait
Bibliographic record
Abstract
Abstract We present numerical results arising from a parameterization of wave-iceinteractions in a two-dimensional ice-ocean model of the Fram Strait (HYCOM:HYbrid Coordinate Ocean Model). The model takes wave predictions/hindcasts fromthe WAM wave model and these waves are advected into the ice, breaking it asthey go. They in turn are attenuated by the ice using the model of Bennetts andSquire (2012). We use a truncated power law for the floe size distribution, following the observations of Toyota et al. (2011). The maximum floe size isdetermined by the dominant wavelength in the ice field. The maximum valueincreases with distance from the ice edge as shorter waves are attenuated morestrongly than long ones. At some distance from the ice edge, breaking is nolonger able to occur, and this marks the end of the Marginal Ice Zone(MIZ). Consequently, we now have a model that predicts the expected floe size andwave intensity at any point in the ice, something that current wave models areunable to do at present, and which is a notable weakness. Recognizing that acombination of large waves and ice can be extremely hazardous, Arctic operatorswho need to know both wave and ice conditions in ice-infested areas will usethe model as a forecasting tool when it is fully operational.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".