Swimming behaviour of downstream migrating carp in accelerating flows
Bibliographic record
Abstract
The downstream migration of fish is impacted by accelerating flow. This study coupled computational fluid dynamics (CFD) modelling and laboratory observations to investigate the effects of two different accelerating flow hydrodynamics on the swimming behaviours of two fish species. The silver carp (Hypophthalmichthys molitrix) and bighead carp (Hypophthalmichthys nobilis) were chosen as target fish species and four hydrodynamic parameters were selected as main indicators to affect fish swimming behaviours. Four specific swimming behaviours were classified in the contraction flume based on fish downstream trajectories. The results indicated that high accelerating flow, low turbulent kinetic energy (Tke) and shear stress generated the symmetric distribution of fish along two boundaries of the contraction flume. It also indicated that discharge increase would increase the fish entrainment risk from intakes to diversion pipe, while two fish species exhibited different swimming behaviour patterns. Velocity and velocity gradient had more significant effects than other hydrodynamic parameters on fish swimming strategies. There was mutual compensation among fish tail-beat frequency, angle and amplitude. The results illustrated the relationship between the swimming behaviours and hydrodynamics, which could contribute to understanding the fish downstream migration behaviours in accelerating flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".