Development of successful fish passage structures for downstream migrants requires knowledge of their behavioural response to accelerating flow
Bibliographic record
Abstract
Fish have evolved intrinsic flight responses, allowing pre-emptive avoidance of potentially threatening situations. To direct downstream migrant fish away from deleterious conditions at dams and other barriers, mechanical devices such as travelling screens and fish bypass systems are often installed. However, field observations suggest that if these structures create areas of rapidly accelerating flow, they do not effectively guide the fish. We studied the avoidance behaviour of actively migrating fall Chinook salmon ( Oncorhynchus tshawytscha ) smolts in controlled experiments of low-, medium-, and high-flow accelerations. We measured the response velocity (VR) and the velocity gradient (VG) over body length. Although VRvaried significantly with flow conditions and increased with increasing water temperature, the median VG at the instant at which smolts displayed an avoidance response was similar over the range of accelerating flows tested. Results from this study present the first quantitative information about the avoidance behaviour of fish to flow acceleration and should provide data needed to help engineers and biologists develop effective systems to alleviate anthropogenically altered flow regimes. Furthermore, the devised experimental setup provides a valuable means to test the effects of accelerating flow on any downstream migrant fish species.
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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".