The use of terrestrial and aquatic microcosms and mesocosms for the ecological risk assessment of veterinary medicinal products
Bibliographic record
Abstract
In this paper, we investigate the applicability of experimental model ecosystems (microcosms and mesocosms) for the ecological risk assessment of veterinary medicinal products (VMPs). VMPs are used in large quantities, but the assessment of associated risks to the environment is limited, although they are continually infused into the environment via a number of routes. It is argued that the experience obtained by pesticide research largely can be used when evaluating VMPs, although there are several major differences between pesticides and pharmaceuticals (e.g., knowledge of their mechanisms of action on nontarget organisms). Also, because microorganisms are often the target organisms of VMPs, risk assessment should focus more on endpoints describing functional processes. This paper provides a review of the current risk assessment schemes of Europe and North America along with examples of experiments already performed with veterinary medicinal products in aquatic and terrestrial ecosystem models. We suggest that some of the approaches developed for pesticide risk assessment can be used for VMPs and offer suggestions for the development of a framework for ecological risk assessment of VMPs.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".