Transforming sustainable aquaculture by applying circularity principles
Bibliographic record
Abstract
Abstract A circular economy is considered one way to reduce environmental impacts of human activities, by more efficient use of resources and recovery, resulting in less waste and emissions compared to linear take‐make‐dispose systems. Muscat et al. developed five ecological principles to guide biomass use towards a circular economy. A few studies have demonstrated environmental benefits of applying these principles to land‐based food systems, but to date, these principles have not been explored in aquaculture. The current study expands on these principles and provides a narrative review to (i) translate them to aquaculture, while identifying implications for the main species and production systems, and (ii) identify the main pathways to make aquaculture more circular. We show that the underlying concepts of the ‘safeguard’, ‘entropy’, and ‘recycle’ principles have been well researched and sometimes well implemented. In contrast, the ‘avoid’ and ‘prioritise’ principles have been explored much less; doing so would provide an opportunity to decrease environmental impacts of aquaculture at the food‐system level. One example is prioritising the production of species that contribute to food and nutrition security, have low environmental impacts and thinking at wider food system scale to avoid feed‐food competition in aquaculture. We identified six priorities that could make aquaculture more circular: (i) increase production and demand for the most essential species, (ii) decrease food loss and waste at farm and post‐harvest stages, (iii) support nutrient recycling practices at multiple scales, (iv) adapt aquafeed formulations, (v) inform consumers about benefits of species of low trophic levels and other environmentally friendly aquatic foods, and (vi) address urgent research gaps.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".