Drift Velocities of Ice Floes in Alaska's Northern Chukchi Sea Flaw Zone : Determinants of Success by Spring Subsistence Whalers in 2000 and 2001
Bibliographic record
Abstract
By March each year, coast-influenced sea ice in Alaska's northern Chukchi Sea consists of the shorefast ice itself plus ice floes moving in a zone that extends from immediately beyond the shorefast ice to coherent pack ice, some 100 km farther offshore. Because westward-drifting polar pack ice encounters fewer landmasses (and less resistance from them) once it passes Point Barrow, a semipermanent polynya or flaw zone dominates coastal ice in this region. Iñupiat residents use open water in flaw leads to hunt migrating bowhead whales from mid-April to early June. Although Iñupiat hunters grasp the nature and importance of ice in motion beyond their horizon, the flaw zone has received less scientific attention than either shorefast ice or polar pack ice farther offshore. Synthetic aperture radar (SAR) satellite imagery is a form of remote sensing recently made available that allows us to address ice movement at a spatial scale familiar to traditional hunters. SAR-tracked ice movements differed between 2000 and 2001, illustrating contrasts between adverse and optimal conditions for spring whaling at Barrow. Case studies of ice-floe accelerations in the two contrasting seasons suggest that many variables influence ice motion. These include weather, seafloor topography, currents, sea-level changes, and events that occurred earlier during an annual accretion of ice. Adequate prediction of threats to ice integrity in the northern Chukchi Sea will require adjustments of our current concepts, including 1) recognizing the pervasive influence of the flaw zone; 2) replacing a focus on vessel safety in ice-dominated waters with an emphasis on ice integrity in high-energy environments; and 3) chronicling ice motions through coordinated ground observation and remote sensing of March-June events in future field studies.
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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) | 0.001 | 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".