Net-chasing training improves the behavioral characteristics of hatchery-reared red sea bream (<i>Pagrus major</i>) juveniles
Bibliographic record
Abstract
The low return rate of fish released for stock enhancement has often been attributed to hatchery-reared fish having inferior behavioral characteristics. We tried to improve the behavioral characteristic of red sea bream (Pagrus major) juveniles by using a net-chasing treatment. The fish were provided with 2 min of net chasing twice daily for 3 weeks, following which their behavioral characteristics (emergence from a start area, avoidance response to novel stimulus, and foraging following transfer between tanks) were individually tested and compared with a control group. A predator exposure test was then conducted using marbled rockfish (Sebastiscus marmoratus). Net-chased fish exhibited a shorter latency to emergence, a higher avoidance rate, and an earlier foraging time than the control fish, indicating that the net-chasing treatment may improve adaptability for environmental change and alertness to a novel object. The net-chased fish also exhibited a better survival rate than the control fish, with an odds ratio of 6.76. We suggest that net-chasing training represents an easy and efficient method for improving the behavior of fish for stock enhancement.
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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".