Winter open-water zone remote sensing (2017-2023) and field (2023) data from the Yukon and Kuskokwim rivers and their tributaries in western Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it