Alaska landfast sea ice: Links with bathymetry and atmospheric circulation
Bibliographic record
Abstract
Using Radarsat Synthetic Aperture Radar (SAR) imagery of northern Alaska and northwestern Canada, we calculated a mean climatology of the annual landfast ice cycle for the period 1996–2004. We also present the monthly minimum, mean, and maximum landfast ice extents throughout the study area. These data reveal where and when the landfast is most stable and which sections of the coast are susceptible to midseason breakout events. Stabilization of landfast ice is strongly related to the advance of the seaward landfast ice edge (SLIE) into waters around 18 m deep. Isobaths near this depth are a good approximation for midseason landfast ice extent. Comparison with work from the 1970s suggests a reduced presence of landfast ice in this region of the Arctic, due to later formation and earlier breakup. This will likely lead to an increase in coastal erosion and may also have profound effects upon subsistence activities, which are intimately linked to the timing of marine mammal migration patterns. Interannually, landfast ice formation correlates with the incursion of pack ice into coastal waters, suggesting that the later mean date of formation in recent years may be related to the increasingly northward location of the perennial sea ice edge. The timing of breakup correlates well with onset of thawing air temperatures. Analysis of regional data shows a multidecadal trend toward earlier thaw onset, which suggests that the observed change in breakup dates may be part of a longer‐term trend.
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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 | 0.002 |
| 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) | 0.002 | 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".