Endocrine-Disrupting Chemicals in Industrial Wastewater Samples in Toronto, Ontario
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
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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