Estimation of Sea Ice Production in the North Water Polynya Based on Ice Arch Duration in Winter During 2006–2019
Bibliographic record
Abstract
Abstract The North Water Polynya (NOW) is the largest recurrent Arctic coastal polynya. The formation of the NOW is critically dependent on the development of an ice arch that defines its northern boundary. In this study, high‐resolution ENVISAT Advanced Synthetic Aperture Radar data, Sentinel‐1A data, and Moderate Resolution Imaging Spectroradiometer data were employed to identify the spatio‐temporal characteristics of the ice arch during 2006–2019. Polynya pixels were identified based on the thin ice thickness (TIT), using a threshold of TIT <0.2 m, from which the polynya extent, heat flux, and ice production (IP) were estimated. The results show the different locations of the ice arch in different years, with a mean duration of 132 ± 69 days. The average annual polynya extent over the 14 years is ∼38.8 ± 8 × 103 km2, and we found that it is more closely correlated with wind speed during the winter and air temperature during early spring. The average heat flux drops from about 248 W/m2 in the winter months to about 34 W/m2 in May. The average accumulated IP varies significantly every year, with an average of 144 ± 103 km3, and peak values in March in most years. No apparent interannual trends are shown for the polynya area, heat flux, and IP during 2006–2019. The results also show that IP calculated based on the ice arch data is approximately 25% lower than that obtained by assuming a fixed time, location, and duration for the polynya.
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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 | 0.000 | 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".