Summation of disinfection by-product CHO cell relative toxicity indices: sampling bias, uncertainty, and a path forward
Bibliographic record
Abstract
The cyto- and genotoxic potencies of disinfection by-products (DBPs) have been evaluated in published literature by measuring the response of exposed Chinese hamster ovary cells. In recent publications, DBP concentrations divided by their individual toxicity indices are summed to predict the relative toxicity of a water sample. We hypothesized that the omission or inclusion of certain DBPs over others is equivalent to statistical sampling bias and may result in biased conclusions. To test this hypothesis, we removed or added actual or simulated DBP measurements to that of published studies which evaluated granular activated carbon as a treatment to reduce the relative toxicity of the effluent. In several examples, it was possible to overturn the conclusions (i.e., activated carbon is detrimental or beneficial in reducing toxicity) by preferentially including specific DBPs. In one example, removing measured haloacetaldehydes caused the predicted cytotoxicity of a treated sample to decrease by up to 47%, reversing the initial conclusion that activated carbon increased the toxicity of the water. We also discuss measurements of statistical error, which are rarely included in publications related to predicted toxicity, but strongly influence the outcomes. Finally, we discuss future research needs in the light of these and other concerns.
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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 | 0.062 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".