Behaviour and ability of a cyprinid (<scp><i>Schizopygopsis younghusbandi</i></scp>) to cope with accelerating flows when migrating downstream
Bibliographic record
Abstract
Abstract The migration corridors in regulated rivers lead downstream fish migrants, particularly juveniles to pass through water infrastructure. Accelerating flow, experienced by fish, might trigger avoidance behaviour and then influence the downstream migration efficacy. It is essential to understand the causes of avoidance behaviour exhibited by downstream migratory fish in accelerating flow. In this study, the effect of three different accelerating flows on the downstream migration behaviour of Schizopygopsis younghusbandi (S.Y) was investigated using a constriction wedge in a circulating flume. The results showed that some fish (30%, 23%, and 39% under low, medium, and high flow conditions, respectively) repeatedly attempted to burst upstream with positive rheotaxis prior to successful passage downstream. Under the low‐, medium‐, and high‐accelerating levels, the average fish swimming speeds were 89.19, 91.28, and 111.94 cm/s, respectively; these values were close to the critical swimming speed (110.42 cm/s) of the target fish. The water velocities at the fish avoidance points were centrally distributed at approximately 73.03 cm/s. Regarding turbulence, the results exhibited that the S.Y generally responded to a discrete range of <50 cm2/s2 of turbulent kinetic energy and < 2 N/m2 of the horizontal component of the Reynolds shear stress (RSSxy). Also, the fish that exhibited avoidance behaviour were not centrally distributed in the lateral and longitudinal velocity locations, where there was an abrupt change in the gradient. This study highlighted the impact of accelerating flow on the downstream fish migration behaviour of a cyprinid. Furthermore, this study quantified the hydraulic factors that triggered this avoidance. Thus, it provided experimental support for optimizing the design of the hydraulic factors for downstream fishways.
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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 | 0.000 | 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".