Occurrence of Endocrine-Disrupting Chemicals in Sewage and Sludge Samples in Toronto, Canada
Bibliographic record
Abstract
Abstract In recent years, many anthropogenic chemicals occurring in the environment have been shown to mimic the action of endogenous hormones. These endocrine-disrupting chemicals (EDCs) can potentially lead to a host of adverse effects on wildlife, such as the feminization of fish, the lack of reproductive success of some species, birth defects, and the development of physical abnormality. In an attempt to establish the levels of contamination by EDCs in municipal sewage and sludge, we have collected samples in the Toronto area and analyzed them for alkylphenols, alkylphenol ethoxylates and carboxylates, 17ß-estradiol and its metabolites, testosterone, bisphenol A, as well as butyltin species. Previously developed methods using solvent extraction, solid-phase extraction, supercritical fluid extraction, GC/MS, and HPLC determination have been used for such samples. Levels of these chemicals varied from <1 ng/L for 17ß-estradiol in the effluent to nearly 900 µg/L for nonylphenol ethoxylates in the influent. Levels of EDCs in sludge also varied widely from >500 µg/g for nonylphenol to <0.1 µg/g for bisphenol A. The screening of many of the above EDCs has become an integral part of an on-going wastewater quality monitoring program in Toronto.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".