The characterization and impact of extreme winds in Nares Strait
Bibliographic record
Abstract
Extreme winds in the Arctic can affect sea ice motion, ocean convection, maritime and aviation activity, and the formation of polynyas. This work investigates a severe wind event that destroyed an ice camp established along Nares Strait in April 2005. We aim to quantify its exceptionality. Nares Strait is a long, narrow body of water between Ellesmere Island (Nunavut, Canada) and northwestern Greenland. There are steep mountains on both sides, significantly impacting meteorological phenomena and making it difficult to model weather events in the area accurately. Therefore, we used the Copernicus Arctic Regional Re-analysis (CARRA) data with 2.5-km horizontal resolution, covering the period 1991-2022, to characterize the wind climate of the region. Our results indicate that the surface winds during the event were associated with a low-level jet and were extreme at certain places and times during the April 2005 storm. The reanalysis generally agrees with the in-situ description of the event, although some of the highest observed wind speeds were not captured in the dataset. There is evidence that Kelvin-Helmholtz (KH) instability may have occurred during the event, which could have contributed to the elevated surface wind speeds. This result has implications for future research expeditions and scientific understanding of the Arctic Climate System, as the wind in Nares Strait controls the flow of most old, thick sea ice that exits the Arctic.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".