Effects of Decelerating and Accelerating Flows on Juvenile Salmonid Behavior
Bibliographic record
Abstract
Abstract Migratory and resident fish species have evolved inherent flight responses to avoid potentially harmful situations. At many dams, fish screens or other structures have been installed to guide fish away from turbines or attract them to routes that will result in higher survival. Avoidance responses of fish to rapidly decelerating and accelerating flows at these structures have been repeatedly observed and can result in ineffective fish guidance. By using controlled flume experiments, we analyzed the avoidance behavior of actively migrating spring Chinook salmon Oncorhynchus tshawytscha smolts in relation to flow decelerations and accelerations. As smolts drifted into areas with decreasing velocities, they actively swam into the current; the larger was the change in water velocity with distance (spatial velocity gradient [SVG]), the faster was the swimming speed exhibited by smolts. Under accelerating flows, the response velocity varied significantly with flow conditions, but the median SVG at which smolts displayed an avoidance response was similar over all flows tested. For both decelerating and accelerating flows, the avoidance response occurred at an SVG of approximately 1 cm·s−1·cm−1. We suggest that this threshold is in part fixed by the energetically optimum swimming speed of the fish (∼1 body length/s).
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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) | 0.001 | 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".