Landfast sea ice break out patterns in the northern Bering Sea observed from C-band Synthetic Aperture Radar
Bibliographic record
Abstract
Arctic sea ice is declining in areal extent and seasonal duration, affecting stable landfast sea ice regimes. These landfast ice regimes are vital platforms for local indigenous subsistence hunters and epontic primary production species supporting local and regional food webs. Under changing thermodynamic and mechanical stresses, landfast ice can become more prone to break out events - unexpected mid-season detachments from the coastline. However, the prevalence of break out events in the landfast ice annual cycle on an interannual and decadal basis is generally understudied. Here, we detect and quantify landfast ice break-out events at St. Lawrence Island in the Northern Bering Sea. We accomplished this using C-band Synthetic Aperture Radar (SAR) imagery to detect landfast ice cover. We also advanced a geospatial analysis method to identify break-out events and distinguish such occurrences from end-of-season breakups. Results reveal that 35 break-outs occurred across four sections of the St. Lawrence Island coastline from 1996 − 2019, with 74 % of break-outs occurring on the northern coastlines. Break-outs generally occurred during annual cycles with higher than average landfast ice cover. During the break-out events, maximum temperatures seldom exceed conditions promoting melt, whereas wind speeds are, on average, 2 m/s faster compared to end-of-season breakup events. However, additional datasets are needed to understand better the influence of tidal amplitude and ocean currents on detected break-out occurrence and location. Our study provides the basis for future research to understand break-out events in landfast ice annual cycles at fine spatial scales unavailable in existing sea ice datasets.
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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 | 0.000 |
| 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) | 0.000 | 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".