Occurrence and Potential Causes of Androgenic Activities in Source and Drinking Water in China
Bibliographic record
Abstract
The increased incidences of disorders of male reproductive tract as well as testicular and prostate cancers have been attributed to androgenic pollutants in the environment. Drinking water is one pathway of exposure through which humans can be exposed. In this study, both potencies of androgen receptor (AR) agonists and antagonists were determined in organic extracts of raw source water as well as finished water from waterworks, tap water, boiled water, and poured boiled water in eastern China. Ten of 13 samples of source water exhibited detectable AR antagonistic potencies with AR antagonist equivalents (Ant-AR-EQs) ranging from <15.3 (detection limit) to 140 μg flutamide/L. However, no AR agonistic activity was detected in any source water. All finished water from waterworks, tap water, boiled water, and poured boiled water exhibited neither AR agonistic nor antagonistic activity. Although potential risks are posed by source water, water treatment processes effectively removed AR antagonists. Boiling and pouring of water further removed these pollutants. Phthalate esters (PAEs) including diisobutyl phthalate (DIBP) and dibutyl phthalate (DBP) were identified as major contributors to AR antagonistic potencies in source waters. Metabolites of PAEs exhibited no AR antagonistic activity and did not increase potencies of PAEs when they coexist.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".