University student perspectives on antimicrobial peptide use in farm animals
Bibliographic record
Abstract
Growing awareness of antimicrobial resistance (AMR) in livestock production has led to calls for the development of alternatives such as antimicrobial peptides (AMPs) that are also able to combat infectious diseases in farm animals. A critical step in the development of AMPs is to understand people's perspectives towards this technology to avoid misalignment with societal expectations. The aim of this study was to investigate university student perspectives of AMP applications in farm animals as alternatives to antibiotic use. We interviewed 20 university students and, using thematic analysis, identified six themes: 1) Initial knowledge, including knowledge of antibiotics and initial impressions of AMPs; 2) Human wellbeing, including the effects of food and animal health on public health, the importance of addressing AMR, and cost for farmers; 3) Animal welfare, including animal health, welfare, and production, and continuation of contentious farm practices; 4) Perceived naturalness of AMPs, including biocompatibility and comparing same and different species transfer of biological material; 5) Unforeseen consequences of AMPs, and the importance of researching unintended consequences of novel technologies; and 6) Public acceptance of AMPs, including trust and lack of awareness. In summary, participants viewed AMPs positively as an alternative to antibiotic use in farm animals to address AMR. However, key concerns centered around unintended harmful effects for food systems, public health, and animal welfare, which may impact public acceptance of AMPs 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".