Juvenile Lake Sturgeon Go To School: Life‐Skills Training for Hatchery Fish
Bibliographic record
Abstract
Abstract Hatchery supplementation of declining fish populations is commonly employed to try to increase year‐class strength. However, the success of such programs is often hampered from low postrelease survival as a result of the failure of hatchery fish to appropriately recognize predation threats. Not surprisingly, there has been considerable effort to train prey to recognize predators prior to release. The objective of our current work was to characterize the antipredator response of hatchery‐reared, predator‐naive young‐of‐the‐year Lake Sturgeon Acipenser fulvescens (an endangered species) to alarm cues from injured conspecifics and test whether these alarm cues could be used to train sturgeon to recognize unknown predators. We found that skin‐derived alarm cues elicited an antipredator response without learning and that learning required cues coming from whole‐body grinds, presumably because they represent a much more reliable indicator of risk. When the experiment was repeated with older sturgeon from Wolf River (Wisconsin), training with cues from whole‐body grinds did not enhance the response. Subjecting the fish to several training sessions (six over 3 d) led to some alteration in behavior. Our results provide insights into how ontogenetic changes (size, scute growth) could explain the different learning outcomes from the larger fish as related to hatcheries and conservation programs. Received June 24, 2015; accepted November 17, 2015
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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) | 0.006 | 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".