Pharmaceuticals in source waters of 95 First Nations in Canada
Bibliographic record
Abstract
OBJECTIVES: Pharmaceuticals are emerging contaminants in the environment. Little has been published about the presence of pharmaceuticals in waterbodies nearby or on reserve land of First Nations in Canada. The objectives of this study were to (1) quantify the level of pharmaceuticals in First Nations' surface waters, (2) calculate the human health risks of the mixtures found, and (3) measure the exposure to pharmaceuticals in First Nations' drinking water where source water was highly contaminated. METHODS: This participatory study measured the levels of 43 pharmaceuticals from surface water samples taken at three water sampling sites chosen by the 95 participating First Nations. The sites were in proximity to recreational areas, fishing areas, drinking water sources, and/or wastewater outflows. When elevated levels of pharmaceutical mixtures were found in samples, drinking water samples were obtained and analyzed for potential pharmaceuticals. Human health risks were calculated by an established protocol. RESULTS: In total, 432 samples were collected at 302 water sampling sites (285 surface water, 11 drinking water, and 6 wastewater sites). Quantifiable levels of 35 pharmaceuticals were found in 79 of the 95 (83%) participating First Nations at 193 of the 285 surface water sites (68%). Overall, the levels found were comparable to or lower than those found in other studies in Canada and worldwide. CONCLUSION: In almost all participating First Nations, there is no human health risk from consuming surface water for drinking. However, surface water in the vicinity of major urban centres should not be used as secondary untreated water sources due to the elevated human health risk associated with exposure to the mixtures of multiple pharmaceuticals detected.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".