Antimicrobial use on 24 beef farms in Ontario.
Bibliographic record
Abstract
Understanding risk factors for antimicrobial resistance requires knowledge of antimicrobial selection pressure. The objectives of this research were to develop methodology for collecting quantitative antimicrobial use information from beef producers in Ontario, to document the types and quantities of antimicrobials reported (for a minimum of 12 mo), and to compare 2 metrics for injectable use reporting. Twenty-four volunteer beef producers were asked to complete a questionnaire, document drug use in a treatment diary, and retain empty medication containers. For injectable antimicrobials, producers recorded approximately 60% of the total use in the treatment diaries; oxytetracycline, penicillin, macrolides, florfenicol, and spectinomycin were used in the greatest quantities. Based on estimated weights of active ingredients (calculated according to number of animals exposed, duration, and average dose per day) the antimicrobials most commonly used in feed were monensin, tylosin, lasalocid, and tetracyclines. The antimicrobials most commonly used in water were lincomycin-spectinomycin, chlortetracycline, and oxytetracycline. Based on estimated weights and measured quantities, < 1% of antimicrobials used were in the Canadian category of highest importance to human medicine. A comparison of animal daily dosages to kilograms of active ingredient demonstrated that the relative ranking of use of antimicrobials varied with the chosen metric, and that further investigation into the best measure in relation to antimicrobial resistance is warranted.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".