Quantifying river migration rates in the Yukon River Watershed from optical satellite imagery, Alaska, 2016-2022
Bibliographic record
Abstract
This dataset describes measurements of river migration rates (averaged over the period 2016-2022) in three locations within the Yukon River Watershed: Huslia, Alaska (AK) (65.700 N, 156.387 W), Beaver, AK (66.362 N, 147.398 W), and Alakanuk, AK (62.685 N, 164.644 W). Huslia is located on the Koyukuk River and Beaver and Alakanuk are located on the Yukon River. The river migration rates are quantified from sub-pixel correlation of optical satellite imagery (Sentinel-2 imagery, 10 meter (m) spatial resolution), following the methodology of Geyman et al. (2024). The methodology allows for the detection of riverbank erosion at scales approximately 5-10 times smaller than the pixel size, so the detection threshold is 1-2 m over the approximately 7-year interval, corresponding to a migration rate of 0.1 to 0.3 m/year. The motion of the eroding and accreting sides of the river are quantified separately. The river migration rate datasets are made available as georeferenced shapefiles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".