A Multi-Time-Scale Assessment of the Influence of Water Releases from the Aishihik Hydropower Plant, Yukon, on Downstream Discharge at Two Distant Gauging Stations
Bibliographic record
Abstract
The release of water from hydropower plants into downstream rivers is primarily driven by electricity demand, creating sub-daily fluctuations in flows, a phenomenon known as hydropeaking. In Southwestern Yukon, the Aishihik plant releases most of its annual volume during the winter, when non-controlled rivers are at their lowest levels. Massive volumes of water released during that period of the year may be enhancing freeze-up ice jams and associated flooding. The present study uses a multi-method approach that includes signal treatments to characterize the influence of water releases from the Aishihik plant on river discharge at selected downstream gauging stations. Overall, this study shows that water releases from the Aishihik plant significantly influence the Dezadeash River discharge at a station located approximately 50 km downstream. The average Dezadeash River discharge is approximately double what its natural level would be during the winter. The effects of hydropeaking from the plant are clearly observable in the Dezadeash discharge sub-daily time series, even during months when plant operations are limited. During the winter, the Alsek River discharge, measured at a station located at more than 150 km from the plant, is also influenced by the Aishihik hydropower plant. The lack of data availability at that station did not allow for an estimation of the degree to which winter discharge may exceed its natural regime.
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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 | 0.000 | 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 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".