Predation, metabolic priming and early life-history rearing environment affect the swimming capabilities of growth hormone transgenic rainbow trout
Bibliographic record
Abstract
The period of first feeding, when young salmonid fishes emerge from natal stream beds, is one fraught with predation risk. Experiments conducted in semi-natural stream mesocosms have shown that growth hormone transgenic salmonids are at greater risk of predation than their non-transgenic siblings, due partly to the higher metabolic demands associated with transgenesis, which force risky foraging behaviours. This raises questions as to whether there are differences in the swim-performance of transgenic and non-transgenic fishes surviving predation experiments. We tested this hypothesis in wild-origin rainbow trout ( Oncorhynchus mykiss ) that were reared from first feeding in semi-natural stream mesocosms characterized by complex hydrodynamics, the presence of predators and oligotrophic conditions. Using an open-flume raceway, we swam fish and measured their capacity for burst-swimming against a sustained flow. We found a significant genotype effect on burst-performance, with transgenic fish sustaining performance longer than their wild-type siblings, both in predator and predator-free stream segments. Importantly, this effect occurred before differences in growth were discernable. We also found that mesocosm-reared fish had greater burst-performance than fish reared in the controlled hatchery environment, despite the latter being unexposed to predators and having abundant food. Our results suggest a potential interaction between predation and metabolic priming, which leads to greater burst capacity in transgenic trout.
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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".