Survival and behaviour of rainbow trout (<i>Oncorhynchus mykiss</i>) released from an experimental aquaculture operationThis paper is part of the series “Forty Years of Aquatic Research at the Experimental Lakes Area”.
Bibliographic record
Abstract
The potential for farmed fish that have escaped from open-cage aquaculture operations to affect native populations will depend on their survival and behaviour in the wild. We used standard commercial practices to rear 10 tonnes of rainbow trout ( Oncorhynchus mykiss ) in a 23 ha lake at the Experimental Lakes Area (Ontario, Canada). Each fall (2003–2005), we released farmed rainbow trout (escapees) into the study lake and monitored their movements using automated positioning telemetry. Rainbow trout experienced high annual mortality (~50%), with none surviving beyond 3 years. Farmed fish had narrowly defined pelagic distributions that comprised the upper few metres of the water column, even when at the cage site. Although released rainbow trout dispersed throughout the study lake, most spent significant portions of time at the cage site, especially during normal operation when commercial feed was available. Core use areas (50% Kernel) included the farm for half of the released fish. Surviving rainbow trout showed continued reliance upon the cage site in their second year. However, wide dispersal, high growth rate, and lack of reliance on the cage site by some escaped fish warrant further research to assess potential effects of open-cage aquaculture in the water bodies where the industry occurs.
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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".