Development of a test method for transformation of veterinary pharmaceuticals and biocides in anaerobic liquid manure
Bibliographic record
Abstract
Abstract Background Spreading of manure on agricultural soils represents an important pathway by which veterinary medicinal and biocidal products enter the environment. To assess their environmental impact in the context of authorization processes, experimental testing of transformation of these compounds in manure is considered in regulatory guidance documents. However, there is no standardized experimental test method available so far. To fill this gap, an experimental test method was developed to examine transformation in liquid cattle and pig manure. Results To account for manure specifics and for a possible influence of manure parameters on transformation rates, a comprehensive data acquisition and statistical analysis were performed. To address the variability of liquid manure of different type and origin, 30 manures were sampled and characterized. Thereof, three cattle and three pig manures were selected to perform anaerobic transformation studies with two radiolabeled veterinary medicinal active substances (14C-salicylic acid and 14C-paracetamol) and a confidential radiolabeled “14C-biocide B” serving as test compounds. Spatial and seasonal variability of manure parameters was quantified, and a sampling technique to obtain homogenous manure samples was developed. Transformation studies with six replicates per sampling point were conducted to examine the influence of test setup, of manure storage conditions, and of parameter variations between manure of the same species and of different species on half-lives, formation of extractable and non-extractable residues and mineralization rates. Finally, a test design was established that yields reproducible results for transformation studies in liquid cattle and pig manure under anaerobic conditions. Conclusions Results give a reliable basis for an experimental test method to perform anaerobic transformation studies in liquid cattle and pig manure. The developed test method comprises detailed guidance on selection of sampling date and site, collection from manure tank, storage duration and temperature in the laboratory, duration of manure acclimation period, and the incubation system design. It proved to give reproducible results in a validation ring test performed in a follow-up project and is planned to be submitted as draft test guideline for approval by the OECD.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".