Promotion of Local Drug Return Programs: A One Health Action to Reduce Freshwater Drug Pollution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concentration of pharmaceuticals in global freshwater sources is a growing wicked problem, with pollution by synthetic estrogens such as 17α-ethinyl estradiol (EE2) becoming a particular concern. Water contamination is largely influenced by anthropogenic sources of pharmaceutical pollution and improperly discarded drugs stemming from a lack of public knowledge regarding correct disposal practices. Active metabolites from these pharmaceutical products can enter environmental surface water and groundwater sources, exerting physiological effects on organisms upon contact. This poses a significant risk to humans, non-human animals, and the environment. While pharmacies in Canada have implemented safe drug return programs, insufficient advertisement has led to low levels of awareness and compliance. In response to these limitations, Pharm-Free Freshwater was created as a grassroots initiative to address the issue of freshwater drug pollution by means of education and the creation of accessible drug return opportunities. This two-step strategy involved a social media campaign to raise awareness about safe pharmaceutical use and disposal in the community. Subsequent outreach events were designed to make pharmaceutical returns more attractive and convenient while continuing to spread awareness. By harnessing the principles of One Health, Pharm-Free Freshwater aims to promote the health and well-being of all stakeholders by advocating for the safeguarding of freshwater ecosystems on a grassroots level.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it