Impact of Resolution on the Representation of the Mean and Extreme Winds Along Nares Strait
Bibliographic record
Abstract
Abstract Nares Strait is the long and narrow strait bounded by steep topography that connects the Arctic Ocean's Lincoln Sea to the North Atlantic's Baffin Bay. The winds that blow along the strait play an important role in modulating ice and water exports from the Arctic Ocean as well as in helping to establish the Arctic's largest and most productive polynya that forms at its southern terminus. However, its remote location has limited our knowledge of the winds along the strait. Here we use weather station data from the region as well as two reanalyses and an operational analysis with nominal horizontal resolutions that vary from ∼30 to ∼2.5 km to characterize the wind field in the vicinity of the strait. The strait has a width that varies from ∼40 to ∼100 km and as such the wind field is typically ageostrophic and controlled by the pressure gradient in the along‐strait direction. We show that model resolution plays a role in the representation of both the mean and extreme winds along the strait through the ability to represent this ageostrophic flow. Higher windspeeds occur in the vicinity of Smith Sound and are associated with a left‐hand corner jet. Kane Basin, the widest section of the strait, is characterized by a gradient in windspeed with higher speeds in the center of the basin and lower winds in the eastern basin that is the result of sheltering by the steep topography of the upstream Washington Land peninsula.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".