Angling-induced injuries have a negative impact on suction feeding performance and hydrodynamics in marine shiner perch, <i>Cymatogaster aggregata</i>
Bibliographic record
Abstract
ABSTRACT Fishing is a popular and lucrative sport around the world and, in some cases, may contribute to declining fish stocks. To mediate this problem and maintain fish biomass in aquatic ecosystems, catch-and-release fishing, whereby a fish is caught and immediately released, has been implemented in many countries. It is unclear whether the injuries to the mouth that are caused by the hook have an impact on feeding performance of fishes. Using high-speed video and computational fluid dynamics (CFD), we asked whether injuries around the mouth caused by fishing hooks have a negative impact on suction feeding performance (measured as maximum prey velocity) of the commonly angled marine shiner perch (Cymatogaster aggregata). We hypothesized that fish with mouth injuries would exhibit decreased feeding performance compared with controls. Ten shiner perch were caught using scientific angling and 10 were caught using a seine net. Feeding events were then recorded at 500 frames per second using a high-speed camera. Compared with the control group, maximum prey velocity was significantly lower in the injured group (P<0.01). Maximum gape, time to peak gape, maximum jaw protrusion and predator–prey distance were comparable between the control and injured groups, leading us to conclude that the injury-induced hole in the buccal cavity wall reduced the pressure gradient during mouth expansion, thereby reducing the velocity of water entering the fish's mouth. This was confirmed with our CFD modelling. Fishing injuries in nature are likely to depress feeding performance of fish after they have been released, although it is currently unclear whether this has a significant impact on survival.
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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".