Pre- and Post-Impoundment Study of Breeding Waterfowl Use of a Hydroelectric Reservoir in the Eastern Canadian Boreal Forest
Bibliographic record
Abstract
Impoundment of hydroelectric reservoirs deeply modifies habitats available for waterfowl because it involves transforming a fast-flowing river, its tributaries and nearby ponds and wetlands into a large body of water. Using a Before-After-Control-Impact design, we evaluated whether the creation of the Péribonka reservoir, a steep-sloped hydroelectric reservoir with low water level fluctuations, affected the abundance and species composition of waterfowl breeding pairs and broods in the area. We used helicopter-based waterfowl survey data covering a period of 2 years before and a period of 10 years after the creation of the reservoir. We also used 9 5x5 km plots and 72 km of river as control sites. Our results show that breeding pair density slightly increased after impoundment, while brood density increased significantly (sixfold), especially for Common Goldeneye (Bucephala clangula). This suggests that there were favorable habitat gains for waterfowl after impoundment, probably due to low water level fluctuations and localized areas of shallow water, and that mitigation measures likely helped to reduce the impact of the project. Because this BACI study ended 10 years after impoundment, it remains difficult to ascertain whether conditions in the Péribonka reservoir have stabilized or are still evolving.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".