Canadian River Hydrokinetic Energy (CRHE) Database
Bibliographic record
Abstract
Many remote and northern communities rely on imported diesel for electricity, facing high costs, logistical challenges, and environmental risks. River hydrokinetic energy (HKE), which generates electricity from flowing water without dams unlike conventional hydropower, offers a lower-impact, locally available renewable alternative. However, identifying promising HKE sites typically requires extensive and costly field data collection, creating a barrier for development. To address this gap, the authors present the first national, high-resolution database of river HKE across Canada, covering all river segments with flows exceeding 100 m³/s. The database includes cross-sectional measurements and estimates of flow, depth, width, velocity, and power at up to 100-m intervals, allowing communities and developers to identify areas with strong local energy potential before committing to field investigations. Validation against field and modelled data confirmed good agreement, supporting its reliability for preliminary site screening and policy planning. By capturing energy-enhancing river features such as lateral constrictions and steep gradients, this dataset provides a valuable tool for accelerating the transition from diesel-based electricity to renewable HKE, particularly in remote and rural communities (https://doi.org/10.1016/j.renene.2025.123970).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.024 |
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".