Evaluating Dam Water Release Strategies for Migrating Adult Salmon Using Computational Fluid Dynamic Modeling and Biotelemetry
Bibliographic record
Abstract
Abstract Hydrodynamics in dam tailraces can influence the swimming behavior and survival of fish, and yet there have been few studies that have linked the movement patterns of fish to encountered flow patterns. In this study, we examined the flow field downstream of Seton Dam in British Columbia, Canada using acoustic Doppler current profilers (ADCPs), and computational fluid dynamic (CFD) modeling of two dam water release scenarios to understand their impacts on the swim speed and behavior of upriver‐migrating adult sockeye salmon (Oncorhynchus nerka). Forty‐five sockeye salmon were tagged with acoustic transmitters and tracked to understand their movement patterns, swim speed, residence time in the dam tailrace, and postdam passage survival. The water release scenarios produced contrasting tailrace hydrodynamics and altered the behavior of migrating sockeye salmon. Tagged fish avoided areas of high velocity (>2.4 m/s or four body lengths/s, twice their critical swim speed), high Reynolds shear stress (>21 Pa), and high turbulence kinetic energy (>0.12 m2 s−2) in the dam tailrace. Excessive use of anaerobic metabolism, together with high water shear stress adjacent to and downstream of the fishway entrance, were thought to be the main cause for low postdam passage survival of sockeye salmon. Additional dam water release strategies were assessed with the CFD model to target releases that improve the migration conditions for sockeye salmon. Our study highlights the importance of pairing hydraulic and ecological information to better understand and improve the migration conditions for wild fish.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".