Pharmaceutically active compounds in Atlantic Canadian sewage treatment plant effluents and receiving waters, and potential for environmental effects as measured by acute and chronic Aquatic Toxicity
Bibliographic record
Abstract
Ten acidic and two neutral pharmaceuticals were detected in the effluents of eight sewage treatment plants (STPs) from across Atlantic Canada. Concentrations varied between nondetectable and 35 microg/L. The analgesic, nonsteroidal anti-inflammatory drugs ibuprofen and naproxen were predominant. Carbamazepine, a neutral compound used as an antiepileptic drug, was observed consistently at a median concentration of 79 ng/L. Acetaminophen was found in the effluents of the three largest mechanical STPs at a median concentration of 1.9 microg/L, but not in the lagoon treatment systems. The substantially longer hydraulic retention times may have contributed to more effective removal of acetaminophen in the lagoon treatment systems. Drugs generally were not detected at significant concentrations in the larger bodies of receiving water (Saint John River, Hillsborough River, and Bedford Bay, Canada). However, drug residues in the small receiving streams were 15 to 30% of the effluent median concentrations. Six compounds (caffeine, naproxen, salicylic acid, carbamazepine, metoprolol, and sotolol) were found to persist in a small stream for a distance of at least 17 km, suggesting that small stream exposure to pharmaceutically active residues may be relatively greater than that in large bodies of water. Bioassays assessing acute and chronic effects on four organisms were conducted on four high-use drugs: Acetaminophen, ibuprofen, naproxen, and salicylic acid (metabolite of acetyl salicylic acid). Results indicated no negative effects except for the chronic algal (Selanastrum capricornutum) growth test on ibuprofen (no-observed-effect concentration, 10 microg/L; lowest-observed-effect concentration, 32 microg/L). Effects of these four compounds on invertebrates and plants in the receiving environments are unlikely based on the concentrations measured.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".