Grape Pomace as a Promising Antimicrobial Alternative in Feed: A Critical Review
Bibliographic record
Abstract
Antimicrobial resistance is among the most urgent global challenges facing sustainable animal production systems. The use of antibiotics as growth promoters and for infectious disease prevention in intensive animal-farming practices has translated into the selection and spread of antimicrobial resistance genes in an unprecedented fashion. Several multi-resistant bacterial strains have been isolated from food-producing animals, thus constituting an alarming food-safety issue. Many industrial byproducts with potential antimicrobial properties are currently being investigated to identify empirical and affordable solutions/alternatives that can potentially be used in feed for animals. Grape pomace is among such byproducts that gained the attention as a result of its low cost, abundance, and, most importantly, its bioactive and antibacterial properties. This review discusses the recently reported studies with regard to exploring the use of grape pomace (and its extracts) in animal production to control pathogens, along with the promotion of beneficial bacterial species in the gut to ultimately alleviate antibacterial resistance. The review further summarizes realistic expectations connected with grape pomace usage and lists the still-to-be-addressed concerns about its application in animal agriculture.
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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 | 0.002 | 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) | 0.005 | 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".