Comprehensive Flow Turbulence Metrics to Improve Bar Rack Guidance for Downstream Migrating Fish
Bibliographic record
Abstract
Abstract Turbulent flows are investigated upstream of a bar rack system that is recommended as optimum in recent literature from tests with several fish species of different morphology, swimming ability, and behavior. Both two‐dimensional two‐component and two‐dimensional three‐component state‐of‐the‐art particle image velocimetry were used to quantify and analyze hydrodynamic metrics important for downstream migrating species. The inclination angles of the bar and rack were 45° and 30°, respectively, and the thickness of the bottom overlay was 13% of the water depth. The two Reynolds numbers investigated, based on incoming velocity and bar thickness, were 4,000 and 6,000. The statistical and structural characteristics of turbulent flows in the streamwise‐spanwise plane at 5% water depth, and the streamwise‐vertical plane at channel mid‐span are discussed. Upstream of the bottom overlay, the mean flow is deflected and accelerated toward the bypass, leading to an increase in the Reynolds stresses, while the turbulence eddies become smaller. For effective fish guidance, it is recommended that sweeping velocity (Vp) be larger than normal velocity (Vn), with Vp parallel and Vn perpendicular to the bar rack and bottom overlay. In the downstream half of the bar rack, Vn may increase sufficiently to surpass Vp near the bypass, possibly reducing effective guidance for some species and sizes. Upstream of the bars, the levels of streamwise mean velocity vary abruptly, which may deter fish from contacting the bars. Although inferences on passage effectiveness are made based on previous studies, tests with different species and sizes are needed to confirm fish responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".