Incidence and Toxicological Significance of Selected Endocrine Disrupting Chemicals (EDCs) in Drinking Water
Bibliographic record
Abstract
Increasing sensitivity of new analytical methods has enabled the detection of certain endocrine disrupting chemicals (EDCs) in drinking water systems. EDCs in raw water entering drinking water treatment plants can arise from many potential sources, including municipal wastewater treatment plant effluent, industrial discharges, agricultural activities, etc. Questions remain about the potential health effects of long-term, low-dose exposures to EDCs via potable water supplies. Of particular concern are potential consequences to sensitive population groups, such as pregnant women and children. We present preliminary data arising from our work on assessing the potential health risks of these chemicals and establishing target concentrations for water treatment. Specifically, we focus on the selection of chemicals of concern (COC) for this project. EDCs were selected as COC for this project on the basis of the following five criteria. (1) Status as an EDC. A literature review was conducted to collect evidence that certain chemicals can be classified as EDCs. Emphasis was placed on chemicals that have produced an adverse effect mediated through the endocrine system in at least one in vivo test system with a laboratory animal that serves as a surrogate for humans or that have been reported to produce an endocrine effect in humans. (2) Likelihood of exposure through drinking water. To assess occurrence in drinking water, monitoring for target chemicals is underway at several drinking water utilities nationwide. A literature search revealed additional occurrence data for source water, raw water, and drinking water, as well as information that can be used to predict the removal of EDCs through drinking water treatment processes. (3) Potential to cause adverse health effects. Severity of effects, potency, and pharmacokinetics (e.g., half-life, bioaccumulation) were considered. (4) Endocrine mode of action. Only those chemicals that act through the so-called "EAT" modes of action (Estrogenic (or anti-estrogenic), Androgenic (or anti-androgenic), and Thyroid-related) were considered. (5) Interest in specific contaminants. The Project Advisory Committee (PAC) and participating utilities were asked to identify EDCs that they believe are potential concerns. EDCs that appear to be the subject of interest among the public and the scientific community also were considered. For selected EDCs, animal and human clinical toxicity data are being examined to establish threshold exposure levels of concern, particularly focusing on the potential for reproductive and developmental effects and effects on endocrine function. The results of this project will provide information to determine whether consumption of EDCs in municipal drinking water poses a public health risk; which chemicals likely present the most significant risks, and which treatment systems most effectively reduce target chemical concentrations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".