Governance Processes and Challenges for Reservation of Antimicrobials Exclusively for Human Use and Restriction of Antimicrobial Use in Animals
Bibliographic record
Abstract
The majority of antimicrobials that are produced are administered to animals, particularly food animals. While the overall impact of antimicrobial use in animals on antimicrobial resistance in humans and the environment is unclear, it undeniably has a role. Yet, some degree of antimicrobial use in animals is necessary for animal health and welfare purposes. Balancing the benefits and risks of antimicrobial use in animals is challenging because of the complexity of the problem and limitations in available data. However, a range of measures can be implemented to reduce, refine and optimize antimicrobial use in animals, with a goal of minimizing the impact on human and environmental health while maintaining necessary therapeutic use in animals. A pandemic instrument can provide the necessary foundation for the whole-of-society and whole-of government One Health approach that is required to strengthen surveillance, communication, collaboration, and action.
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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 | 0.084 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| 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".