Factors affecting the decision-making process of using insect-based products in animal feed formulations
Bibliographic record
Abstract
Insect meals are promising alternative feed ingredients although their application is still not commonplace. Their inclusion requires the consideration of various factors to optimise growth, animal welfare, and feed costs. The insect meal form (whole or defatted) impacts the level of inclusion, in particular in feeds where low amount of lipids is needed (e.g. poultry). From a nutritional point of view, the factors that influence the insect meal characteristics include insect species, rearing substrates and production processes. Processing (drying, defatting) can dramatically influence the nutrient digestibility and availability that requires assessment through in vivo or in vitro trials, with differences being observed in relation to the entity of the defatting process as well. The inclusion of full-fat or defatted meal may impact the final product quality (fatty acid profile). Low digestibility of chitin is also a limiting factor. Studies to increase the digestibility of insect meals using additives are ongoing. For these reasons, when different insect protein suppliers are used for feed production, chemical analyses need to be performed. In addition to the nutritional aspect, in some species (i.e. fish), a physical evaluation of the feed is necessary. In particular, the high fat content of whole larvae meal may increase the mixture viscosity and decrease the pellet stability, resulting in nutrient loss. Palatability affects feed ingestion; though insect meals seem well accepted, some palatability issues have been reported at high inclusion levels. It is however not clear if these issues are due to the level of inclusion or to some intrinsic characteristics of the meal used. Finally, the crucial factor for the future practical incorporation of insect meals in animal feeds is the availability and consistency of the supply. Without large and consistent quantities, it will be difficult for feed producers to incorporate these alternative ingredients within their production processes.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".