Toward Applying a Circularity Framework Against the Use of Aquaculture Feed Ingredients
Bibliographic record
Abstract
This review explores feed sustainability and examines how circularity principles might apply to aquaculture under the proposed European Union framework. The framework includes: 1) minimizing the use of food-grade resources as feed, 2) minimizing reliance on land use, 3) maximizing the use of locally sourced ingredients, and 4) optimizing the nutritional characteristics of ingredients. To examine these issues of circularity, there was a focus on the broader challenges affecting feed ingredient utilization, rather than on individual ingredient or ingredient class. This required consideration of how the feed sector currently navigates the complexities of ingredient use and to understand the key drivers behind that commercial process. From this understanding, it becomes evident that ingredient characterization is important. Accordingly, there was a focus on some of the key issues in the characterization process and identify challenges that need to be addressed moving forward. The allocation of resources to food or feed is another pillar of the framework. There was examination of some of the associated issues, with examples from the grains, fishery and salmon aquaculture sectors. One of the key drivers of circularity is the need to improve sustainability of feed ingredient use. Exploration was undertaken of what this means, how to assess it, and what progress has been made in setting standards for consistent sustainability reporting and assessment. A case study was also examined to explore alternative approaches to the use of by-products in the circularity framework, including the development of bioactive ingredients that enhance efficiencies and create options for alternative ingredient use. To conclude a consideration was made of the importance of communication in the circularity story and discuss who holds the responsibility for communicating these messages.
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 | 0.102 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.011 | 0.017 |
| 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".