Simulating trawl herding in flatfish: the role of fish length in behaviour and swimming characteristics
Bibliographic record
Abstract
Abstract Theoretical modeling indicates that the herding efficiency of flatfish by bottom-trawl sweeps is highly sensitive to subtle changes in fish behaviour. Yet the degree of variation in herding behaviour within a species, population, or individual remains poorly understood because of the difficulty observing and measuring fish behaviour in this region of the gear. The purpose of this study was to simulate herding under laboratory conditions in order to examine the behaviour and swimming characteristics of flatfish. Using a large flume equipped with a moving floor, we examined the effect of fish length on gait use, behaviour, and swimming kinematics in American plaice (Hippoglossoides platessoides). While swimming at a speed comparable to the herding speed of trawl sweeps (0.3 m s−1), smaller plaice (<30 cm) spent a large percentage of time using the kick-swim gait, while larger fish (≥30 cm) preferred cruising. In total, 65% of plaice exhibited settling behaviour, analogous to the swim-and-settle behaviour observed in response to trawl sweeps. The frequency of this behaviour and the distance swum between settles were independent of fish length. Only the frequency of gliding changed with the duration of swimming. Like other teleost species, tailbeat frequency decreased with increasing fish length. The results from this study indicate that fish length affects gait use and swimming kinematics in flatfish, but not the frequency of gliding and settling behaviours. These observations support the hypothesis of size-selective herding and provide further insight into the herding efficiency of trawl sweeps.
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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 | 0.001 | 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".