Explorative behavior increases vulnerability to angling in hatchery-reared brown trout (<i>Salmo trutta</i>)
Bibliographic record
Abstract
Animals, including fish, display individually consistent behavioral differences that may affect an individual’s vulnerability not only to predation, but also to fishing. Compared with complex natural environments, plain hatchery environments might induce development of behaviors that increase vulnerability to fishing, which would in turn have major implications for the management of stocks by supportive releases. We studied whether the vulnerability of hatchery-reared brown trout (Salmo trutta) to angling could be predicted by rearing method (standard versus enriched) or behavioral variation that was assessed using long-term observations of moving activity in groups. High moving activity in the beginning of the behavioral tests (i.e., exploration behavior) predicted increased vulnerability to angling independently of fish body size. Standard rearing promoted high exploration rate among fish, whereas enriched rearing promoted improvement in body condition in (semi-)natural conditions. However, the driving influence of hunger on vulnerability could not be ruled out, as the most explorative standard-reared fish appeared unable to maintain their body condition during the experiments. This study provides direct evidence that standard hatchery rearing method promotes behaviors that directly predict vulnerability to angling.
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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".