Air-Sea, Ice-Sea, and Effective Wind Forcing of the Beaufort Gyre
Bibliographic record
Abstract
Surface heat and momentum fluxes between the atmosphere and ocean are mitigated by sea ice cover, resulting in an effective net forcing that can be very different in character from the wind stress alone. The effective stress is often expressed as a weighted sum of air-sea and ice-sea stresses. This is appropriate for levitating ice. Allowing instead for floating ice, one can rewrite the effective forcing in a way that makes no explicit mention of the ice-ocean stress. Instead, the net forcing becomes a linear sum of air-sea and internal ice stresses. These differences are explored in the context of the Beaufort Gyre. Previous studies have introduced the ice-ocean governor as a regulating mechanism for the gyre, and in this limit, the ice-ocean stress is assumed to vanish. For floating ice, the governor limit can be thought of instead as a balance between the wind stress and the internal ice stress. Note that this balance would seem to be unlikely in that the internal stress is associated with small-scale linear kinetic features, which are very different in character from the mesoscale and synoptic features that determine the wind stress. High-resolution ECCO data will be used to examine the instantaneous and time-averaged spatial structure of the various terms that drive the Beaufort Gyre. Future work will also examine the air-sea-ice interface in different wind and ice regimes, as well as the role of eddy fluxes in the gyre dynamics.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".