Seasonality effects on pharmaceuticals and <i>s</i>-triazine herbicides in wastewater effluent and surface water from the Canadian side of the upper Detroit River
Bibliographic record
Abstract
The influence of seasonal changes in water conditions and parameters on several major pharmacologically active compounds (PhACs) and s-triazine herbicides was assessed in the wastewater and sewage treatment plant (WSTP) effluent as well as the downstream surface water from sites on the Canadian side of the upper Detroit River, between the Little River WSTP and near the water intake of a major drinking water treatment facility for the City of Windsor (ON, Canada). The assessed PhACs were of neutral (carbamazepine, cotinine, caffeine, cyclophosphamide, fluoxetine, norfluoxetine, pentoxifylline, and trimethoprim) and acidic (ibuprofen, bezafibrate, clofibric acid, diclofenac, fenoprofen, gemfibrozil, indomethacin, naproxen, and ketoprofen) varieties. The major assessed s-triazine herbicides were atrazine, simazine, propazine, prometon, ametryn, prometryn, and terbutryn. At sampling times from September 2002 to June 2003, 15 PhACs were detected in the WSTP effluent at concentrations ranging from 1.7 to 1244 ng/L. The PhAC concentrations decreased by as much 92 to 100% at the Little River/Detroit River confluence because of the river dilution effect, with further continual decreases at sites downstream from the WSTP. The only quantifiable s-triazine in WSTP effluent, atrazine, ranged from 6.7 to 200 ng/L and was higher in Detroit River surface waters than in WSTP effluent. Only carbamazepine, cotinine, and atrazine were detectable at the low-nanogram and subnanogram levels in surface waters near a drinking water intake site. Unlike the PhACs, atrazine in the Detroit River is not attributable to point sources, and it is heavily influenced by seasonal agricultural usage and runoff. Detroit River surface water concentrations of carbamazepine, cotinine, and atrazine may present a health concern to aquatic wildlife and to humans via the consumption of drinking water.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".