The future of genetic engineering to provide essential dietary nutrients and improve growth performance in aquaculture: Advantages and challenges
Bibliographic record
Abstract
Advancements in gene technology in recent years have been driving the aquaculture industry forward. Improvements in growth performance, feed efficiency, and omega‐3 content are goals of the industry that could capitalize on applications of genetic engineering. One of the major challenges in the industry is to reduce the use of fish meal and oil, to improve the environmental and economic sustainability of aquaculture. The recent development of genetically engineered feed ingredients is one potential solution to the looming problem of fish meal and oil dependency. Furthermore, the development of transgenic fish has potential to improve production efficiency and other future desirable characteristics that relate to feed utilization and product quality. New gene technologies are beginning to revolutionize how we produce our food, and in aquaculture, will ultimately reduce pressure on wild fish stocks, help to preserve natural aquatic ecosystems, and improve nutritional profiles of farmed fish for human consumption. The purpose of this review is to provide an update on the current applications of genetic engineering technology to improve aquaculture through nutrition, including the development and use of transgenic feed ingredients, transgenic fish, and ultimately their impacts on nutrition, product quality, and consumers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".