Freeze‐Up Ice Jams and Channel Hydraulics Cause Hazardous Open Water Zones Within Winter Ice Cover on the Kuskokwim and Yukon Rivers and Their Tributaries
Bibliographic record
Abstract
Abstract Timing and completeness of freeze‐up on northern rivers impact winter travel and indicate responses to climate change. Open‐water zones (OWZs) within ice‐covered rivers are hazardous and may be increasing in extent and persistence. To better understand the distribution, variability, and mechanisms of OWZs, we selected nine reaches totaling 380 river‐km for remote sensing analysis and field studies in western Alaska. We initially identified 48 OWZs from November 2022 optical imagery, inventoried their persistence into late winter and interannual consistency over previous years, and at a subset measured ice thickness, water depth and velocity, and physicochemistry. The most consistent locations of OWZ formation occurred below sharp bends and channel constrictions, whereas locations associated with river bars and eroding banks were more transient. Of 359 OWZs identified in early winter over 6 years, 8% persisted into late winter―all on the Yukon River mainstem. Although several OWZs were in locations where we anticipated groundwater influence, we found no field data indication of groundwater upwelling. Observations of jumble ice upstream of many OWZs led us to examine freeze‐up ice jam locations in optical imagery, which showed strong correspondence to downstream OWZs. We hypothesize that reaches downstream of ice jams are much slower to freeze‐over due to restricted ice transport and high turbulence caused by channel form and ice‐affected hydraulics. Future work should focus on evaluation of this and other competing hypothesis at both reach and river network scales to predict OWZ locations and occurrence relative to other processes affecting river freeze‐up in northern climates.
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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 | 0.001 | 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".