Review: Use of Animal Fats in Aquaculture Feeds
Bibliographic record
Abstract
Formulated aquaculture feeds are among the most expensive animal feeds on the market. These feeds are often high in lipids, the bulk of which is generally provided by fish oil. Because of its high cost and potential long-term supply problems, it is now widely acknowledged that fish oil should be used more sparingly in aquafeeds. Rendered animal fats are economical lipid sources that have been used in fish feeds for decades but their use has been greatly limited for various reasons, such as poor digestibility and nutritive value, and more recently, fear of disease transmission. Recent studies have indicated that rendered animal fats can be valuable ingredients in fish feeds. Incorporation levels equal to 30-40% of total lipid of the diet do not impose any negative effects on growth performance, feed efficiency, and product quality of most fish species studied. However, the diet must contain sufficient levels of unsaturated fatty acids (mono and polyunsaturated) to allow for proper digestibility of saturated fatty acids, and, obviously, meet essential fatty acid requirements of the fish. The use of rendered animal fats at the expenses of fish oil in aquafeeds could immediately result in significant savings for feed manufacturers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".