Semi-automated classification of river ice types on the Peace River using RADARSAT-1 synthetic aperture radar (SAR) imagery
Bibliographic record
Abstract
The winter regime of the Peace River in northern British Columbia and Alberta is a determining factor for the operation of the British Columbia Hydro and Power Authority Williston Reservoir. Therefore, fine beam RADARSAT-1 synthetic aperture radar (SAR) satellite images were acquired in winter and spring 2000 and 2001 and analyzed for ice cover types. 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 (i) visually and (ii) using an unsupervised Fuzzy K-means classification. To streamline the process in an operational environment, the unsupervised ice classification was semi-automated. The unsupervised classification broke the data into seven classes, which represent the major ice cover types observed on the Peace River. The spatial distribution of ice cover types as generated by the ice classification generally coincides well with air-truth information and backscatter signatures from SAR images. The location of the boundaries between the ice types appears to be accurate, but it may not be precise. The ice maps can be used for ice monitoring, decision-making purposes, or the verification of ice models.Key words: river ice, remote sensing, RADARSAT-1, Peace River, semi-automated classification.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".