Winter open-water zone remote sensing (2017-2023) and field (2023) data from the Yukon and Kuskokwim rivers and their tributaries in western Alaska
Bibliographic record
Abstract
Timing and completeness of freeze-up on northern rivers impacts safe winter travel and may indicate responses to climate change. Open-water zones (OWZs) within ice-covered rivers are hazardous partly because their unpredictability and are suggested to be increasing in extent and persistence due to groundwater upwelling, higher winter discharge, and permafrost degradation. To better understand the distribution, variability, and mechanisms of winter OWZs, we selected nine study reaches totaling 400 kilometers (km) of the Yukon and Kuskokwim rivers and their tributaries for remote sensing analysis and field studies in western Alaska, USA. We identified 51 OWZs from late November optical imagery along these reaches ranging from 60 meters (m) to 9 km in length, inventoried their persistence over six years, and at a subset measured ice thickness, under-ice water depth and velocity, water-column and river-bed physico-chemistry. Concurrently, we investigated if and to what extent sediment was entrained in river ice at these same sites. These locations corresponding to observed OWZs were quantified by size, classified by hydrogeomophic location, and tracked for consistency during the preceding five years in the early (late November) and late (late February or early March) winter periods. A subset of these OWZ were visited in March of 2023 to collect additional field data on snow, ice, and physico-chemistry including ice sediment concentration. This research is part of the Fresh Eyes on Ice and Sediment Ice Learning on the Tanana (SILT) projects.
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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) | 0.001 | 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".