Fish Nutrition and Current Issues in Aquaculture: The Balance in Providing Safe and Nutritious Seafood, in an Environmentally Sustainable Manner
Bibliographic record
Abstract
Global aquaculture production has increased in recent years and it is predicted that aquaculture will provide the most reliable supply of seafood in the future.However, there are many controversial issues in aquaculture regarding food safety, nutrition, and sustainability; many of which are directly related to the nutrition and feeds for farmed fish.These nutrition-related issues must be considered in order to achieve balance in safe and nutritious food production and sustainability in aquaculture.This review highlights recent studies and discusses new and innovative aspects in fish nutrition.Some issues in the area of fish nutrition require consideration and improvement, such as: feed and nutrient efficiency, overfeeding and waste, fish meal and fish oil replacements, fish health, biotechnology, and human health concerns.The findings reviewed in this manuscript demonstrate promise toward improvement of the aquaculture industry through nutrition.This review is an update in fish nutrition research, and provides insight on the progression and evolution of this field in order to meet the needs of the industry with the purpose to achieve a balance in seafood production and environmental sustainability.The outcome of this review encourages the use of biotechnology as a tool to meet seafood production and environmental sustainability, in order to ensure global food security in the future and to improve our resource use.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".