Analysis and dissipation of the antiparasitic agent ivermectin in cattle dung under different field conditions
Bibliographic record
Abstract
Cattle treated with the veterinary parasiticide ivermectin fecally excrete residues. The authors report the exposition and dissipation characteristics of these residues in dung of ivermectin-treated cattle and in soil beneath this dung on pastures in Canada, France, Switzerland, and The Netherlands. Residues were quantified for dung collected from cattle after 3 d, 7 d, 14 d, and 28 d posttreatment and subsequently exposed in the field for up to 13 mo. The authors optimized a high-performance liquid chromatography– fluorescence detection method to detect ivermectin residues in dung and soil matrices. They showed that a solid phase extraction and purification step generally can be eliminated to reduce the time and cost of these analyses. They also found that the addition of water to relatively dry samples improves the extraction efficiency of residues. They then analyzed the field samples to document differences in ivermectin dissipation in cattle dung among sites, with 50% dissipation times of up to 32 d and 90% dissipation times >396 d. They further showed that the dissipation characteristics of residues are comparable between dung of ivermectin-treated cattle and dung to which ivermectin has been added directly. Lastly, they report the first use of a desorption electrospray ionization–high-resolution–mass spectrometric method to detect residues of metabolites in a dung matrix.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".