40 years on: what do we know about drinking water disinfection by-products (DBPs) and human health?
Bibliographic record
Abstract
2014 marks the 40th anniversary of the seminal discovery by Johannes Rook, in 1974, that trihalomethanes (THMs) were formed by the chlorination of natural organic matter (NOM) in drinking water. Since this discovery, which revolutionized how we viewed drinking water safety and quality, hundreds of other classes of disinfection by-products (DBPs) have been discovered. The finding in 1976 by the US National Cancer Institute that chloroform, the dominant THM, was a rodent carcinogen spurred a large number of epidemiology and toxicology studies into chlorinated drinking water. In 1985, this cancer finding was shown to be wrong. We should now be asking: What do we know about the human health impacts of DBPs in drinking water? Bladder cancer has been the most consistent finding from epidemiologic studies in North America and Europe and the possibility that chlorinated drinking water contributes an increased risk of bladder cancer remains a viable hypothesis. Despite some recent improvements in exposure assessments to focus on inhalation and dermal exposures rather than ingestion, no causal agent with sufficient carcinogenic potency has been identified, nor has a mechanistic model been validated. Consequently, a sensible precautionary approach to managing DBPs remains the only viable option based on four decades of evidence.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".