From farm management to bacteriophage therapy: strategies to reduce antibiotic use in animal agriculture
Bibliographic record
Abstract
To reduce the use of antibiotics in animal agriculture, a number of effective or commercially viable alternatives have been implemented by food animal producers or are under development. Perhaps the most well-established strategies are flock and herd management practices to mitigate disease introduction and spread, and, subsequently, reduce the need for antibiotic use. While vaccines in food animal production have been used to prevent both bacterial and viral diseases, but historically, most vaccines have targeted viral diseases. Though vaccines against viral diseases can help reduce the need for antibiotic use by controlling the spread of secondary bacterial infections, more recent vaccines under development specifically target bacteria. New developments in selecting and potentially tailoring bacteriophages provide a promising avenue for controlling pathogenic bacteria without the need for traditional small-molecule antibiotics. In this article we discuss these established and emerging strategies, which are anticipated to reduce the reliance on antibiotics in food animal production and should reduce the prevalence and transmission to humans of antimicrobial resistant bacteria from these systems.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".