Effects of vegetation on hydrodynamics and swimming behaviour of yellow catfish ( <i>Pelteobagrus fulvidraco</i> )
Bibliographic record
Abstract
Understanding the influence of vegetation on fish swimming behaviour is crucial for effective habitat management and conservation efforts. This study investigates the impact of submerged vegetation on fish behaviour and hydrodynamics through laboratory observations and computational fluid dynamics modelling. The results demonstrate that fish exhibit diverse swimming behaviours in flumes with both vegetation and smooth zones. Fish swimming near the vegetation zone experience slower velocities and lower turbulent kinetic energy levels than those near the smooth zone. Moreover, fish swimming near the junction between the vegetation and smooth zones encounter elevated TKE levels along their movement trajectories, requiring increased tail-beat frequencies to overcome turbulence during upstream swimming. The presence of vegetation in the channel generates regions characterized by reduced flow velocities and diminished turbulence, which offer energy-saving opportunities for fish. These findings contribute to the development of effective habitat management strategies for maintaining and enhancing fish populations in vegetated aquatic ecosystems.
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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 | 0.002 | 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) | 0.000 | 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".