The Use of Pomace as Animal Feed: A Review of Grape and Tomato Pomace
Bibliographic record
Abstract
Pomace is generated in large quantities yearly; high water content and bulkiness make it difficult to be easily disposed of thereby contributing to environmental pollution and providing breeding space for flies which can transmit diseases. Incorporating the pomace generated from grape and tomato fruits in animal nutrition will improve sustainable agriculture; the animals will also benefit from the polyphenols in the pomace which can improve their antioxidant status thereby improving animal health and welfare. Pomace consists of unfermentable sugars, tannins, anthocyanins, lycopene, and cellulose which have natural antioxidants, anti-inflammatory and antimicrobial properties. This review focused on the utilization of grape and tomato pomace as feedstuff for animals, the knowledge gap in the use of pomace in animal nutrition was also outlined. Supplementation of pomace generated from grape and tomato was evaluated on animal growth and reproductive performance, health, oxidative stress, animal products and gut health. In conclusion, incorporating agro-industrial byproducts into animal diet can be beneficial to farm animals by improving their health, welfare, performance as well as the environment, thereby leading to a more sustainable agricultural practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".