Hydrokinetic resource assessment for the Canadian Arctic for turbine-based power generation
Bibliographic record
Abstract
ABSTRACT Renewable energy development has rekindled interest in hydrokinetic power production using zero-head turbines. This study estimates the hydrokinetic power potential for current-based systems in the Canadian Arctic, primarily Nunavut, for the current 2001–2020 and near-future 2021–2040 periods, based on streamflow obtained from an ultra-high-resolution climate-hydrology modeling system for a high emission scenario. A comparison of simulated hydrographs with available observations suggests good agreement, with the Nash Sutcliffe efficiency coefficient in the 0.85–0.96 range. Spatial patterns of hydrokinetic power estimates, which are similar to that of flow velocity, indicate a potential of above 100,000 kW for river reaches in central Nunavut for current/future climates. Investigation of the number of days with flow velocities surpassing the 1.5 m/s threshold for turbine functionality, considering also the impact of river ice using a simplified approach, confirms segments of central basin rivers as promising sites for hydrokinetic turbine placement. This foundational work is crucial in informing detailed site-specific investigations to support the implementation of hydrokinetic energy conversion systems. This will be of interest for remote communities in the Canadian Arctic where decentralized power production from renewable energy sources is being considered as an economically viable option in offsetting the high cost of diesel-based power production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".