Towards achieving circularity and sustainability in feeds for farmed blue foods
Bibliographic record
Abstract
Abstract The aims of this review are to describe the role of ‘blue‐food production’ (animals, plants and algae harvested from freshwater and marine environments) within a circular bioeconomy, discuss how such a framework can help the sustainability and resilience of aquaculture and to summarise key examples of novel nutrient sources that are emerging in the field of fed‐aquaculture species. Aquaculture now provides >50% of the global seafood supply, a share that is expected to increase to at least 60% within the next decade. Aquaculture is an important tool for reducing resource consumption in global protein production and increasing resilience to climate change and other global disruptions (i.e. pandemics, geo‐political instability). Importantly, blue foods also provide essential nutrition for a growing human population. Blue foods are helping to help the global goal of ‘zero hunger’ (United Nation's Sustainable Development Goal 2) while reducing the dependency on finite natural resources but further refinement and new solutions are needed to make the industry more ‘circular’ and sustainable, particularly with respect to sourcing raw materials for aquafeeds. This review describes the feed resources that are available or may be created within a circular bioeconomy framework, their role within the framework and in aquaculture and ultimately, how these resources contribute to de‐risking and establishing a resilient aquaculture production chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".