Behavioral attributes of turbine entrainment risk for adult resident fish revealed by acoustic telemetry and state-space modeling
Bibliographic record
Abstract
Fish entrainment through turbine intakes is one of the major issues for operators of hydropower facilities because it causes injury and/or mortality and adversely affects population abundance. Entrainment reduction strategies have been developed based on the behavior of downstream migrating fishes, particularly diadromous species. However, knowledge of the behavior of migratory fishes has very limited application for reducing the entrainment of resident fishes, including several species that represent important recreational and aboriginal fishery resources in reservoirs. In this study, we used fine-scale acoustic telemetry and state-space modeling to investigate behavioral attributes associated with entrainment risk of resident adult bull trout ( Salvelinus confluentus ) in a large hydropower reservoir in British Columbia, Canada. We found that adult bull trout resided longer in the vicinity of the powerhouse and moved closer to the turbine intakes in the fall and particularly in the winter. Bull trout were more likely to engage in exploratory behavior (characteristic of foraging or reduced activity) during periods when their body temperature was lower or higher than 6°C. We also detected diel changes in behavioral attributes, with bull trout distance to intakes and probability of exploratory behavior slightly increasing at night. We hypothesize that the exploratory behavior in the forebay is associated with foraging for kokanee (nonanadromous form of Oncorhynchus nerka ), which have been shown to congregate near the dams of hydropower reservoirs in the winter. Our study findings should be applicable to bull trout populations residing in other reservoirs and indicate that entrainment mitigation (for example, use of deterrent devices) should be focused on the fall and winter. This work also provides a framework for combining acoustic telemetry and state-space models to understand and categorize movement behavior of fish in reservoirs and, more generally, in any environment with fluctuating water levels.
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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 | 0.000 | 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".