Growth of rainbow trout (<i>Oncorhynchus mykiss</i>) in warm-temperate lakes: implications for environmental change
Bibliographic record
Abstract
To predict potential effects of climate and anthropogenic impacts on fish growth, we compared growth rates of rainbow trout (Oncorhynchus mykiss) in nine closely located warm-temperate lakes of contrasting morphometry, stratification and mixing regime, and trophic state. Analyses of long-term mark–recapture data showed that in deep oligotrophic and mesotrophic lakes, trout growth rates increased with increasing indices of lake productivity. In contrast, in shallow eutrophic lakes, where fish habitat volume is constrained by temperature and dissolved oxygen, trout growth rates declined with increasing productivity. Growth rates were higher in lakes with greater volumes of favourable habitat (i.e., dissolved oxygen > 6.0 mg·L−1and temperature < 21 °C) and lower in lakes with increased turbidity, chlorophyll a, and nitrogen concentrations. Our findings suggest that increases in lake productivity and temperatures as a result of global climatic change are likely to be more detrimental to salmonid habitat quality in shallower, productive lakes, while salmonids will better endure such changes in deeper, oligotrophic lakes. Fishery managers can use this information to aid future stocking decisions for salmonid fisheries in warm-temperate climates.
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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".