Drinking Water Disinfection Byproducts (DBPs) and Human Health Effects: Multidisciplinary Challenges and Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While drinking water disinfection has effectively prevented waterborne diseases, an unintended consequence is the generation of disinfection byproducts (DBPs). Epidemiological studies have consistently observed an association between consumption of chlorinated drinking water with an increased risk of bladder cancer. Out of the >600 DBPs identified, regulations focus on a few classes, such as trihalomethanes (THMs), whose concentrations were hypothesized to correlate with the DBPs driving the toxicity of disinfected waters. However, the DBPs responsible for the bladder cancer association remain unclear. Utilities are switching away from a reliance on chlorination of pristine drinking water supplies to the application of new disinfectant combinations to waters impaired by wastewater effluents and algal blooms. In light of these changes in disinfection practice, this article discusses new approaches being taken by analytical chemists, engineers, toxicologists and epidemiologists to characterize the DBP classes driving disinfected water toxicity, and suggests that DBP exposure should be measured using other DBP classes in addition to THMs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it