Endocrine-Disrupting Chemicals in Industrial Wastewater Samples in Toronto, Ontario
Bibliographic record
Abstract
Abstract The occurrence of endocrine-disrupting chemicals (EDCs) such as bisphenol A (BPA), 4-tert-octylphenol (OP), nonylphenol (NP) and its ethoxylates (NPEO) in wastewater generated in the Toronto area has been studied. In all, 97 samples from 40 facilities in ten different industry classes have been collected and analyzed. Widely divergent concentrations have been observed in these samples. They ranged from <0.01 to 195 µg/L for OP, from <0.1 to 253 µg/L for NP, from <2 to 117,570 µg/L for NPEO, and from <0.01 to 149 µg/L for BPA. The results show that the concentrations of NP and NPEO in these samples generally exceeded City of Toronto By-law (No. 457-2000) limits. The results also suggest that detergents based on NPEO are still extensively used by the commercial laundries, and also by the textile products and clothing industries. These facilities, together with several sources in the chemical and chemical products industries and the fabricated metal products industries are believed to be the major sources of NP and NPEO input into the sewer system in Toronto. In addition to the two facilities in the chemicals and chemical products sector, several commercial laundries also had significant on-site releases of BPA. Except for those collected from three facilities in the chemicals and chemical products industries, the levels of OP in these samples were generally low. Many industries in the Toronto area would have to take drastic actions to reduce releases of NPEO and NP if full compliance with the most recent City By-law regarding wastewater quality were to be achieved.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".