Fruit pomaces—their nutrient and bioactive components, effects on growth and health of poultry species, and possible optimization techniques
Bibliographic record
Abstract
The ever-growing human population, coupled with the exigent need to meet the increasing demand for poultry meat and egg, has put the onus on poultry nutritionists and farmers to identify alternative feed ingredients that could assure the least-cost feed formulation. In addition, the public desire for non-antibiotic-treated poultry products has also necessitated the ultimate search for potent antibiotic alternatives for use in poultry production. While some identified alternatives are promising, their cost implications and technical know-how requirements may discourage their ease of adoption in poultry. The use of plants and/or their by-products, like fruit pomaces, present a pocket-friendly advantage and as a result, are gaining much interest. This is traceable to their rich phytochemical profile, nutritional composition, ready availability, and relatively cheap cost. The fruit juice and wine pressing industries generate a plethora of fruit wastes annually. Interestingly, fruit pomaces contain appreciable dietary fibre, protein, and phenolic compounds, and thus, their adoption could serve the poultry industry in dual capacities including as substitutes to antibiotics and some conventional feedstuff. Thus, there is a possibility to reduce fruit wastes produced and feed-cost in poultry farming from environmental and economical standpoints, respectively. This review seeks to provide reinforcing evidence on the applicability and impact of fruit pomaces in poultry nutrition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".