Aggression and feeding of hatchery-reared and naturally reared steelhead (<i>Oncorhynchus mykiss</i>) fry in a laboratory flume and a comparison with observations in natural streams
Bibliographic record
Abstract
We quantified the aggression and feeding of naturally reared steelhead (Oncorhynchus mykiss) fry stocked into a laboratory flume with naturally reared fry or hatchery-reared fry from conventional and enriched rearing environments at three densities in the presence and absence of predators, and compared the aggression and feeding observed in the flume to that observed in two streams. Steelhead fry attack rate increased with density and was reduced in the presence of predators, but was not affected by rearing treatment. Threat rate appeared to increase with density and was significantly affected by rearing treatment combination, but was not significantly affected by predator presence. Feeding rate was not affected by density or rearing treatment, but was reduced in the presence of predators. The rate of aggression by steelhead fry in two streams was lower than that observed in the laboratory and did not increase with density. Rates of aggression and feeding of hatchery-reared and wild steelhead fry were not significantly different in the streams. Overall, we found no evidence that hatchery rearing environments caused higher aggression in steelhead fry. Laboratory observations of salmonid aggression, particularly at high density, may not reflect aggression levels in the wild.
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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 | 0.001 |
| 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".