How to test nontarget effects of veterinary pharmaceutical residues in livestock dung in the field
Bibliographic record
Abstract
To register veterinary medicinal products (VMPs) as parasiticides on pastured animals, legislation in the European Union requires an environmental risk assessment to test the potential nontarget effects of fecal residues on dung-dwelling organisms. Products with adverse effects in single-species laboratory tests require further, higher-tier testing to assess the extent of these effects on entire communities of dung-dwelling organisms under more realistic field or semifield conditions. Currently, there are no documents specifically written to assist researchers in conducting higher-tier tests or to assist regulators in interpreting the results of such tests in an appropriate context. Here we provide such a document, written by members of the SETAC Advisory Group DOTTS (Dung Organism Toxicity Testing Standardization) with research experience on dung fauna in central and southern Europe, Canada, Australia, and South Africa. This document briefly reviews the organisms that make up the dung community and their role in dung degradation, identifies key considerations in the design and interpretation of experimental studies, and makes recommendations on how to proceed.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".