Pharmaceutical pollution: A weakly regulated global environmental risk
Bibliographic record
Abstract
Abstract The effects of pharmaceuticals in the environment (PiE) are well evidenced, yet from an environmental perspective, pharmaceuticals remain weakly regulated internationally. Pharmaceutical pollutants are addressed under the Strategic Approach to International Chemicals Management (SAICM), a global framework for the management of chemicals and wastes. We provide an overview of the state of knowledge on PiE and identify international efforts targeting the regulation of PiE, as well as gaps in regulation and how SAICM could address them. ‘Environmentally persistent pharmaceutical pollutants’ (EPPP) was adopted under SAICM as an issue of concern 6 years after ‘nanotechnologies and manufactured nanomaterials’. Our analysis draws on this field to inform the development of policy approaches to EPPP. While there is significant cooperation underway on PiE, initiatives are highly fragmented. The post‐2020 SAICM may help bridge the gaps. Further development should be informed by experiences from nanomaterials, particularly with respect to definitions, information gathering and building more integrative approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".