A logic-driven assessment to refine SAR-based river ice classifications
Bibliographic record
Abstract
Abstract Radar remote sensing provides useful information to differentiate river ice conditions and ice cover types in large rivers. However, false classifications are common, especially at the end of winter, due to water on ice as well as wet snow. These situations can present challenges to end users, such as water resources managers and flood forecasters. In this study, we design a logic-driven assessment to refine existing classifications to distinguish between areas of water or wet snow on ice and open water. It is uncommon for river segments to experience ice cover, followed by open water, then ice cover again, within three consecutive radar images. Our decision tree analysis therefore assumes that river segments that are classified as water, but classified as ice in the radar images before and after, represent water or wet snow on ice. We examine the potential of this approach on two rivers in the Yukon Territory, Canada. The Äshèyi Chù (Aishihik River) is a narrow, regulated river with a relatively steep slope (0.3%) and commonly experiences flood issues at freeze-up. The Chu kon’ dëk (Yukon River at Dawson) is a much larger, low gradient (0.04%) river with a history of ice jam related flooding. Pixels are tested based on this concept, and a clustering approach is applied to reduce noise. The success of the algorithm is assessed using drone imagery and Sentinel-2 optical imagery. We show that using logic can offer ways to refine river ice classification, that is meaningful to the end user.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".