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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".