Effects of simulated water level management on shore erosion rates. Case study: Baskatong Reservoir, Québec, Canada
Bibliographic record
Abstract
This study was carried out as part of a feasibility study on the construction of a hydroelectric generating station at Mercier dam at the outlet of the Baskatong reservoir in Québec. The study dealt mainly with a comparison of the current and future bank erosion rates of the reservoir. Given the year of impounding of the Baskatong reservoir, i.e., 1927, it was possible to track bank erosion rates using field surveys and photogrammetric and numerical analyses. It was noted that in certain areas, such as Du Diable Bay and Windigo Bay, the Baskatong reservoir banks were substantially affected by erosion. Estimates indicate that the banks have receded by about 10 to 15 m over a 20-year period. The banks most affected by erosion are those containing sandy material of fluvioglacial or glaciolacustrine origin with bluffs greater than 2 m in height and characterized by the longest fetches. In one section of Windigo Bay, banks had receded by about 30 m in 20 years. Wave action is the main factor responsible for bank erosion, especially in areas highly exposed to long fetches. In fact, there is a correlation between bank erosion rates, fetch distribution, and the erosive energy of waves. It has been noted that receding banks with the highest erosion rates are linked to the greatest wave erosion energy. Based on the values obtained using simulated reservoir water levels, while taking into account the future construction of a new generating station, bank erosion rates appear to be comparable to those measured under actual conditions, though erosion rates could increase by about 2830% in the areas most affected by erosion at the present time.Key words: bank erosion, waves and fetch, erosion rates estimated, Baskatong Reservoir, Québec, Canada.
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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) | 0.001 | 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 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".