Potential risks of trophic impacts by escaped transgenic salmon in marine environments
Bibliographic record
Abstract
SUMMARY There is significant concern about potential ecological effects of introduced organisms, including non-indigenous species and those created by genetic modification. This paper presents an Ecopath with Ecosim modelling approach, designed to examine long-term trophic effects of growth hormone (GH) transgenic coho salmon should they ever escape to a coastal salmonid ecosystem, namely the Strait of Georgia in British Columbia (Canada). The model showed that the effects of introduced GH transgenic coho salmon varied with their biomass, diet, structure of the invaded ecosystem, and environmental conditions. Occasional escapes of non-reproductive salmon did not have a significant impact on the example ecosystem. However, effects of GH coho salmon varied with their diet when large numbers of these fish were present in the simulated ecosystem (for example, when they constituted 20% of total current aquaculture production in the area). Further, climate-driven changes in the biomass of low trophic levels (bottom-up effects) could have a greater impact on the ecosystem than the introduction of large numbers of GH coho salmon. A new version of Ecopath with Ecosim's Monte Carlo approach showed that the model predictions were robust to GH coho salmon's Ecopath parameters, but more sensitive to vulnerabilities of prey to GH coho salmon. Modelling ecosystem effects of genetically modified organisms provides a complementary approach for risk assessments when data from nature are not readily obtainable.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".