Flow Constrictions and Fish Behavior: Unraveling Undulatory Swimming Strategies in Accelerating Flow
Bibliographic record
Abstract
Section contractions in rivers often induce accelerated flows in nature, exerting substantial influence on the swimming behaviors of various fish species. This study focuses on the undulatory swimming strategies of fishes and the effects of hydrodynamics on the swimming behavior in a flume with constriction. By using a combination of computational fluid dynamics (CFD) modeling, convolutional neural network (CNN), and observation of fish behavior with kinematic and morpho-kinematic parameters, we investigate how fish select their swimming strategies when encountering complex flow conditions. The results reveal that fish exhibit distinct swimming behaviors in response to accelerating flows, including avoidance behavior, drifting with the flow, and escaping upstream. Fish demonstrate a preference for swimming on one side of the flume, with the side bank of the adaptation region being the most favored location due to the lower flow velocity and thigmotactic behavior. Additionally, we identify three swimming gaits employed by the fish to navigate complex river environments, adjusting their swimming behavior based on environmental cues. This study highlights the significance of both head and tail characteristics in governing fish swimming behavior, with head features crucial for perceiving the environment and tail movements directly influencing swimming speed and maneuverability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".