Antimicrobial resistance and the environment: assessment of advances, gaps and recommendations for agriculture, aquaculture and pharmaceutical manufacturing
Bibliographic record
Abstract
A roundtable discussion held at the fourth International Symposium on the Environmental Dimension of Antibiotic Resistance (EDAR4) considered key issues concerning the impact on the environment of antibiotic use in agriculture and aquaculture, and emissions from antibiotic manufacturing. The critical control points for reducing emissions of antibiotics from agriculture are antibiotic stewardship and the pre-treatment of manure and sludge to abate antibiotic-resistant bacteria. Antibiotics are sometimes added to fish and shellfish production sites via the feed, representing a direct route of contamination of the aquatic environment. Vaccination reduces the need for antibiotic use in high value (e.g. salmon) production systems. Consumer and regulatory pressure will over time contribute to reducing the emission of very high concentrations of antibiotics from manufacturing. Research priorities include the development of technologies, practices and incentives that will allow effective reduction in antibiotic use, together with evidence-based standards for antibiotic residues in effluents. All relevant stakeholders need to be aware of the threat of antimicrobial resistance and apply best practice in agriculture, aquaculture and pharmaceutical manufacturing in order to mitigate antibiotic resistance development. Research and policy development on antimicrobial resistance mitigation must be cognizant of the varied challenges facing high and low income countries.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".