Feedlot Cattle Antimicrobial Use Surveillance Network: A Canadian Journey
Bibliographic record
Abstract
Antimicrobial drugs are important tools for maintaining human and animal health. Globally, antimicrobial use (AMU) in food-producing animals is under increasing scrutiny due to its potential to promote antimicrobial resistance (AMR). Historically, comprehensive Canadian data related to the types of antimicrobial drugs used, extent of use, common indicators of use and the demographics of the cattle populations receiving antimicrobial drugs have been limited, in part due to segmentation in the cattle industry and fragmentation of the drug distribution system. Appropriate AMU estimates are required to understand AMU practices, to interpret AMR levels and patterns, to meaningfully assess associated public health risks, and to inform stewardship activities. The Canadian beef cattle industry has a long history of collaboration in AMU and AMR research. Prior research projects identified both opportunities and challenges in the collection of AMU data. Cornerstone projects provided insight into the complexity of collecting AMU data in Canada's feedlot sector. This paper will discuss how the lessons learned from past work have contributed to the formation of a Canadian fed-cattle antimicrobial surveillance program that was initiated in 2019. This important surveillance program will allow feedlot cattle AMU to improve management decisions and support AMU best practices in the evolving Canadian AMR landscape.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".