Towards effective nutritional management of waste outputs in aquaculture, with particular reference to salmonid aquaculture operations
Bibliographic record
Abstract
Long-term sustainability of many fish culture operations may be dependent on their ability to reduce their waste outputs. The release of solid wastes is mainly a function of the digestibility of various dietary components, and the release of dissolved wastes is mainly a function of the metabolism of nutrients by the fish. Consequently, simple principles of nutrition and models have been effectively used to describe, predict and manage the excretion of wastes by fish. Nutritional strategies offer a direct and effective way of managing waste output by aquaculture operations. Very significant reduction in waste outputs per unit of fish produced, notably in terms for solid and phosphorus wastes, have been achieved over the past few decades by commercial fish culture operations. Further reduction in waste outputs could be achieved through fine-tuning of feed formulations, judicious use of feed additives and processing/refining of ingredients. A better understanding of the basis of the effect of various endogenous (biological) and exogenous (dietary, environmental) factors on nutrient utilization by fish could also contribute to the development of strategies for reducing waste outputs. The present paper provides a brief overview of issues and challenges related to potential environmental impacts of wastes, and of recent progresses relative to nutritional strategies aimed at better management of the release of wastes by aquaculture operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".