Agriculture and Agri-Food Canada’s research program on antimicrobial resistance
Bibliographic record
Abstract
A key strategy for attenuating the development of antimicrobial resistance (AMR) is ensuring judicious use of antimicrobials in human and veterinary medicine and in agriculture. Research on AMR in agriculture includes risk assessment, risk management, and identifying the role of agricultural practices in development of AMR. Risk assessment includes an impact assessment of antimicrobial use in livestock and on the environment; for example, many antimicrobials are excreted unchanged and thus reach the environment through manure application. This creates the potential for AMR transmission through the food processing chain and into agro-ecosystems receiving the agricultural waste. Risk management includes the assessment of cost-effective methods to keep animals healthy without the need for antimicrobial use, such as the use of vaccines, nutritional supplements and pre-, pro- or synbiotics and of waste management strategies to avoid AMR transmission. Currently, there is an important gap in understanding the degree of human exposure to AMR that is generated through agriculture, the burden of illness of AMR pathogens in human populations and the relationship between exposure and burden of illness. It is important that research on the agricultural, environmental and human medicine dimensions of AMR not be undertaken in silos, which is why the United Nations and countries around the world are working together within the One Health Framework that considers the inter-relatedness of humans, animals and the environment.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".