Antimicrobial resistance and the environment: assessment of advances, gaps and recommendations for agriculture, aquaculture and pharmaceutical manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it