Growth‐enhanced coho salmon invading other salmon species populations: effects on early survival and growth
Bibliographic record
Abstract
Summary The first genetically modified ( GM ) fish intended for human consumption has recently stimulated significant scientific discussion and regulatory scrutiny regarding food safety and environmental risks. Currently, no experiments with transgenic fish have been performed in nature, yet such data are needed to facilitate predictions of ecological consequences should engineered fish escape to the natural environment. To address this limitation, we conducted experiments under natural conditions but within a contained environment to assess the impact of invasion of growth‐enhanced GM coho salmon O ncorhynchus kisutch ( W albaum) on survival and growth of three naturally cohabitating fishes: C hinook salmon O . tshawytscha ( W albaum), steelhead trout O . mykiss ( W albaum) and conspecific wild‐type coho salmon. We found that the impact of stream‐reared GM coho salmon on invaded specimens was similar to the impact of non‐ GM coho salmon. However, GM fish significantly reduced survival and growth of the invaded populations if they were first allowed to grow larger under hatchery conditions before being released. Synthesis and applications . Our results show that the ecological impact of fish genetically modified ( GM ) for rapid growth on closely related fish species may not be high in stream environments, unless these fish are first reared under culture conditions where they are able to realize their genetic growth potential. As such, first generation escapes of GM fish into the natural environment should be a main concern in the short term, whereas later generations, which are more similar to naturally occurring genotypes, are expected to have significantly weaker effects but which could persist for longer periods.
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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) | 0.002 | 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".