Identification of River Ice Types on the Peace River using RADARSAT-1
Bibliographic record
Abstract
The winter regime of the Peace River in northern British Columbia and Alberta is a determining factor for the operation of BC Hydro's Williston Reservoir. Therefore, several Fine Beam RADARSAT-1 SAR satellite images were acquired in winter / spring 2000 and 2001, and analyzed for ice types and ice-front locations. Video footage of the ice conditions on the Peace River was obtained from aerial ice observations that were conducted simultaneously with the image acquisitions. The analysis of the images was done 1) visually and 2) using an Unsupervised Fuzzy K-means Classification. By visually analyzing the images, several river ice features could be identified, including frazil pans, frazil floes, shore ice composed of frazil slush, melting ice, and brash. Juxtaposed and secondary consolidated ice could be distinguished relative from each other. Under most conditions encountered in this study, the location of the ice front coincided with the head of the complete ice cover, and was, therefore, discernible from the SAR images. Ambiguities arose from open water and freeze-over border ice. By comparison, the Unsupervised Classification broke the data into four classes, which represent river signatures ranging from open water to ice with a strong radar return. No physically existing ice types could be assigned to the three categories representing ice features. However, the spatial distribution of ice features closely matches the visible ice types on the river. User interpretation is required to derive ice types from the classification maps. This study suggests that the visual analysis of SAR imagery is very powerful for river ice type classifications, but that more effort needs to be made to further develop automated river ice classification techniques. 1.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".