Stranded Kokanee Salvaged from Turbine Intake Infrastructure Are at Low Risk for Reentrainment: A Telemetry Study in a Hydropower Facility Forebay
Bibliographic record
Abstract
Abstract Entrainment at hydropower facilities, where fish (volitionally and nonvolitionally) enter hydropower infrastructure such as intake towers, can lead to fish becoming stranded for considerable periods of time rather than being flushed to downstream areas. To reduce fish injury and/or mortality from entrainment stranding events, hydropower operators will salvage stranded fish and release them back into the upstream reservoir. We documented the postrelease movements of salvaged fish to determine their vulnerability to reentrainment at a large hydropower facility. Kokanee Oncorhynchus nerka were collected from the turbine intake towers at the W. A. C. Bennett Dam in northeastern British Columbia, surgically implanted with small acoustic transmitters, and released in the forebay area of the hydropower facility. Fish movements were tracked using an array of hydrophones in the forebay area. While the depths and hydraulics of the forebay resulted in low detection efficiency of the receiver array, detection data for 25 fish revealed that 72% (n = 18) of fish were last detected at hydrophones located >1,000 m from the turbine intakes (considered low risk to restranding or reentrainment), 24% (n = 6) of fish were last detected at hydrophones <500 m to the turbine intakes (considered vulnerable to restranding), and one reentrainment event (n = 1; 4% maximal entrainment rate) was observed. Our results indicate there is a low risk associated with kokanee reentrainment events at this large hydropower facility and that manual salvage appears to be a reasonable approach to mitigate fish loss.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".