Determination of veterinary pharmaceuticals in poultry litter and soil by methanol extraction and liquid chromatography-tandem mass spectrometry
Bibliographic record
Abstract
Pharmaceuticals are emerging contaminants with potential risks to the environment and human health. A liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was developed for determination of the antimicrobials virginiamycin, monensin, salinomycin, narasin and nicarbazin in poultry litter and soil. This method involves methanol extraction and clean-up of extracts through glass microfibre filters, introduction of the extracts and separation of compounds on a Zorbax Eclipse XDB C8 column, and compound detection in a Quattro Micro Micromass spectrometer. For litter samples, Method Detection Limits ranged from 0.1-0.6 microg Kg(-1), while Limits of Quantitation (LOQs) were 2, 1, 0.4, 1 and 2 microg Kg(-1) for virginiamycin, monensin, salinomycin, narasin and nicarbazin, respectively. For soil samples calculated LOQs were 2, 3, 1, 1, and 1 microg Kg(-1) for virginiamycin, monensin, salinomycin, narasin and nicarbazin, respectively. Application of the LC-MS-MS method for detection of veterinary pharmaceuticals in litter collected from commercial poultry farms showed that compounds were present at concentrations ranging from 10-11,000 microg Kg(-1).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".