A Screening Level Index for Assessing the Impacts of Veterinary Medicines on Dung Flies
Bibliographic record
Abstract
Veterinary parasiticides are administered to livestock to control a wide range of parasites. Following excretion, these substances may persist in the environment and impact nontarget organisms. This paper describes a simple screening-based index for predicting the effects of veterinary parasiticides on dung flies using data on parasiticide toxicity, animal husbandry, and parasiticide use. The utility of the index has been assessed, at the farm scale for a number of dipteran species, using data from a survey of farms in England and insect ecology and ecotoxicological data. The results indicate that a large proportion (35%) of parasiticide treatments in England will have no impact on dung fly populations. In terms of individual parasiticides, the macrocyclic lactone doramectin was predicted to have the highest impact on English dipteran populations with a maximum reduction in the population of horn flies on one farm of 28%. Ivermectin pour-on had the next highest impact (6.8%), followed by eprinomectin (6.4%), and ivermectin injection (4.1%). Due to a lack of data, it was not possible to assess the effects of the benzimidazole parasiticides (oxfendazole and fenbendazole), morantel and permethrin. The approach is simple, nondata-intensive and has the potential to be a valuable tool for use in environmental risk assessment or management of new and existing veterinary parasiticides.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".