A Scoping Study of Livestock Antimicrobials in Agricultural Streams of Alberta
Bibliographic record
Abstract
Pharmaceuticals are commonly used in the livestock industry, and studies have demonstrated that some pharmaceuticals can reach receiving water bodies. The main objective of this study was to measure the occurrence and concentration of selected veterinary pharmaceutical compounds commonly used by the livestock industry in Alberta across streams varying in agricultural intensity. A total of 247 water samples were collected from 23 watersheds during the open water season between, May 2005 and May 2006. Samples were analyzed for 27 commonly used veterinary pharmaceuticals from the following compound classes: avermectins, beta lactams, fluoroquinolones, ionophores, lincosamides, macrolides, sulfonamides, and tetracyclines. Subsequently, 10 antimicrobials were detected: narasin, salinomycin, monensin, sulfamethazine, sulfathiazole, sulfadoxine, erythromycin, lincomycin, chlortetracycline, and oxytetracycline. Trace (ng·L–1) concentrations of antimicrobials were detected in 51% of the samples (127 out of 247 samples). Monensin and sulfamethazine were detected most frequently (34% and 8% of samples, respectively). Monensin concentrations ranged from below the detection limit of 2 ng·L–1 to 843 ng·L–1. Monensin detection frequencies in study watersheds were correlated to manure production percentiles and regional cattle densities. Monensin concentrations were found to be significantly correlated to several water quality parameters (e.g., NH3–N). Monensin detection frequencies were found to be significantly higher in the spring than in the fall. Maximum concentrations for the other nine antimicrobials detected ranged between 3 and 250 ng·L–1. The antimicrobials detected and their concentrations were found to be similar to those in some recent European and North American livestock pharmaceutical stream surveys. Findings from this study can inform future monitoring programs and provides ambient concentrations for environmental (fate and transport) and toxicity studies to better evaluate potential risks to humans and the receiving aquatic ecosystems.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".