DRINKING WATER DISINFECTION BYPRODUCTS:CHEMICAL CHARACTERIZATION AND TOXICITY
Bibliographic record
Abstract
Disinfection of drinking water is essential to and effective for the prevention of water-borne diseases.While achieving deactivation of microbial pathogens to minimize the acute risk,an unintentional consequence is the formation of disinfection byproducts(DBPs) resulting from reactions of disinfectants with natural organic materials(NOMs) in water.Epidemiological studies show potential associations of increased risk of bladder cancer and adverse health effects with DBPs.Balancing microbial risk with chemical risk is challenging for public health protection.Currently,the most commonly used disinfectants are chlorine,chlorine dioxide,chloramines and ozone.Here we provide an overview of chemical characterization,toxicity and analytical methods of 10 classes of DBPs,including trihalomethanes(THMs),haloacefic acids(HAAs),bromate(BrO-3),chlorite(ClO-2),haloacetonitriles(HANs),mutagen X(MX),halonitromethane(HNMs),iodo acids(IAs),N-nitrosodimethylamines(NMs) and halobenzoquinones(HBQs).We will discuss the challenges and opportunities in the research of DBPs and health effects.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".