Predator-recognition training of hatchery-reared walleye (<i>Stizostedion vitreum</i>) and a field test of a training method using yellow perch (<i>Perca flavescens</i>)
Bibliographic record
Abstract
Fishes reared in captivity are predator-naïve and suffer large predation mortality when stocked into lakes with a full complement of predators. We tested the potential of predator training to enhance post-stocking survival of hatchery-reared walleye (Stizostedion vitreum). In the first part of the study, we found that walleye (i) use chemical cues for assessing predation risk, (ii) do not have innate recognition of the odor of northern pike (Esox lucius) as an indicator of predation, and (iii) associate predation risk with pike odor after a single simultaneous encounter with pike odor and chemical alarm cues from walleye skin. In the second part of the study, we attempted to mass-train yellow perch, Perca flavescens (as a surrogate for walleye), to fear pike odor. Perch response to pike odor was not changed by placing sponge blocks containing pike odor and perch alarm cues around the perimeter of a pond. On pre- and post-training assays, perch avoided traps scented with perch alarm cues, but did not avoid traps labeled with pike odor or water. We conclude that recognition training offers potential as a management tool for walleye, but significant logistic challenges must be solved before it can be implemented.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".