Could probiotics be the panacea alternative to the use of antimicrobials in livestock diets?
Bibliographic record
Abstract
Probiotics are most frequently derived from the natural microbiota of healthy animals. These bacteria and their metabolic products are viewed as nutritional tools for promoting animal health and productivity, disease prevention and therapy, and food safety in an era defined by increasingly widespread antimicrobial resistance in bacterial pathogens. In contemporary livestock production, antimicrobial usage is indispensable for animal welfare, and employed to enhance growth and feed efficiency. Given the importance of antimicrobials in both human and veterinary medicine, their effective replacement with direct-fed microbials or probiotics could help reduce antimicrobial use, perhaps restoring or extending the usefulness of these precious drugs against serious infections. Thus, probiotic research in livestock is rapidly evolving, aspiring to produce local and systemic health benefits on par with antimicrobials. Although many studies have clearly demonstrated the potential of probiotics to positively affect animal health and inhibit pathogens, experimental evidence suggests that probiotics' successes are modest, conditional, strain-dependent, and transient. Here, we explore current understanding, trends, and emerging applications of probiotic research and usage in major livestock species, and highlight successes in animal health and performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".