Hazard assessment of the veterinary pharmaceuticals monensin and nicarbazin using a soil test battery
Bibliographic record
Abstract
Veterinary pharmaceuticals are widely used as food additives in the poultry industry, and the unknown consequences of releasing these compounds into the environment are of concern. The purpose of the present study was to determine the direct impact of 2 veterinary pharmaceuticals (nicarbazin and monensin), commonly used in the poultry industry, on nontarget invertebrates and plant species. Ecotoxicological tests were used to evaluate the acute and chronic toxicity in earthworms (Eisenia andrei), collembolans (Folsomia candida), and 2 plant species (Brassica rapa and Triticum aestivum). Chemical analytical measurements were in good agreement with the nominal concentrations used, although some variability was seen. The results obtained showed no effects of nicarbazin at the highest nominal tested concentration of 1000 mg a.i./kg soil dry weight on any of the organisms, whereas exposure to monensin caused a concentration-specific response pattern. Species sensitivity to monensin decreased in the following rank order: B. rapa > T. aestivum > E. andrei > F. candida, with measured median effect concentrations (based on soil exposure) ranging between approximately 10 and 120 mg/kg. Our results emphasize the importance of using a test battery when assessing ecotoxicological effects by using different ecophysiological endpoints and species from different trophic levels. Environ Toxicol Chem 2018;37:3145-3153. © 2018 SETAC.
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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) | 0.001 | 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 | 0.001 | 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".