Analytical discovery of water disinfection byproducts of toxicological relevance: highlighting halobenzoquinones
Bibliographic record
Abstract
Analytical advancement enables discoveries in water research, and challenges in the identification and determination of a wide range of trace level toxic compounds in water drives the development of new analytical platforms and tools. The identification of toxic disinfection byproducts (DBPs) in disinfected drinking water is an excellent example. Water disinfection is necessary to protect the public from waterborne disease. However, an unintentional consequence is the formation of DBPs resulting from reactions of disinfectants with natural organic matter in source water. To date, regulated DBPs do not account for the increased bladder cancer risk estimated in epidemiological studies. The majority of halogenated DBPs remain unidentified and the discovery of unknown DBPs of toxicological relevance continues to be a major focus of current water research. This review will highlight halobenzoquinones as a class of DBPs that serves as an example of analytical development and toxicological studies. We will feature recent trends and gaps in analytical technologies for identification of unknown DBPs and bioassays for evaluation of the toxicological effects of specific DBPs and their mixtures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".