Detection and drivers of exposure and effects of pharmaceuticals in higher vertebrates
Bibliographic record
Abstract
Pharmaceuticals are highly bioactive compounds now known to be widespread environmental contaminants. However, research regarding exposure and possible effects in non-target higher vertebrate wildlife remains scarce. The fate and behaviour of most pharmaceuticals entering our environment via numerous pathways remain poorly characterized, and hence our conception and understanding of the risks posed to wild animals is equally constrained. The recent decimation of Asian vulture populations owing to a pharmaceutical (diclofenac) offers a notable example, because the exposure route (livestock carcasses) and the acute toxicity observed were completely unexpected. This case not only highlights the need for further research, but also the wider requirement for more considered and comprehensive 'ecopharmacovigilance'. We discuss known and potential high risk sources and pathways in terrestrial and freshwater ecosystems where pharmaceutical exposure in higher vertebrate wildlife, principally birds and mammals, may occur. We examine whether approaches taken within existing surveillance schemes (that commonly target established classes of persistent or bioaccumulative contaminants) and the risk assessment approaches currently used for pesticides are relevant to pharmaceuticals, and we highlight where new approaches may be required to assess pharmaceutical-related risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".