Veterinary antimicrobials in cattle feedlot environs and irrigation conveyances in a high-intensity agroecosystem in southern Alberta, Canada
Bibliographic record
Abstract
Abstract The South Saskatchewan River Basin (SSRB) is considered one of the most intensively farmed regions in Canada, with high densities of livestock and expansive areas of irrigated cropland. We measured concentrations of seven veterinary antimicrobials (VAs) in 114 surface water samples from feedlot environs and 219 samples from irrigation conveyances in the SSRB. Overall, detection frequencies in feedlot environs were 100% for chlortetracycline (CTC) and tetracycline (TC), 94% for monensin (MON), 84% for tylosin (TYL), 72% for lincomycin (LIN), 66% for erythromycin (ERY), and 23% for sulfamethazine (SMZ). For irrigation conveyances, detection frequencies for CTC and TC remained high (94–100%), but dropped to 18% for ERY, 15% for TYL, 10% for MON, and 4% for SMZ. Lincomycin was not detected in irrigation conveyance water. Maximum concentrations of VAs ranged from 1384 µg L−1 (TC) to 17 ng L−1 (SMZ) in feedlot environs while those in irrigation conveyances were 155 ng L−1 (TC) to 29 ng L−1 (ERY). High detection frequencies and median concentrations of VAs in both feedlot environs and irrigation conveyances were associated with high amounts of precipitation. However, an irrigation district (ID) with high livestock density (Lethbridge Northern) did not exhibit higher concentrations of VAs compared to IDs with less livestock, while levels of VAs in irrigation conveyances were less influenced by the degree of surface runoff. The ubiquity of CTC and TC in our study is likely a reflection of its widespread use in intensive livestock operations. Additional investigation is required to link environmental concentrations of VAs with livestock densities and increase our understanding of potential antimicrobial resistance in high-intensity agroecosystems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".