An approach for Canadian river hydrokinetic resource assessment with synthetic aperture radar satellite images
Bibliographic record
Abstract
• Hydrokinetic Resource Assessment with SAR Satellites (HyRASS) is introduced. • One classifier identifies whitewater river sections in summer imagery. • One classifier identifies river ice and open water in winter imagery. • Whitewater, ice, and water combined to identify persistent, stable, high-flow water. • Persistent, stable, high-flow water is required for hydrokinetic turbine deployment. This paper describes a novel approach that uses Synthetic Aperture Radar (SAR) satellite imagery to identify river sections with persistent, stable, high-flow water. Such sections represent candidate sites for the generation of green electricity through the deployment of hydrokinetic turbines. Canada-wide mapping of potential hydrokinetic turbine sites requires Earth Observation satellite technology. The application of SAR satellites is advantageous because of their capacity to acquire high-quality images independent of weather and light conditions. Our SAR image approach includes two classifiers. The first identifies whitewater river sections in images acquired during summer. The second uses images acquired during winter to identify river ice and open water. Both classifiers are developed using a machine-learning algorithm that identifies classification thresholds in a multidimensional space made up of advanced SAR image parameters. The whitewater classifier makes use of backscatter and image texture parameters to achieve 92.6% accuracy. The ice–water classifier makes use of backscatter in two polarizations and a polarimetric decomposition to achieve 89.0% accuracy. In combination, the classes identified enable the mapping of river sections with persistent, stable, high-flow water, i.e., candidate hydrokinetic turbine sites. Our SAR image approach is named HyRASS—Hydrokinetic Resource Assessment with SAR Satellites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".