Modeling formation and control of disinfection byproducts in chlorinated drinking waters
Bibliographic record
Abstract
While disinfection of drinking water reduces the risks of pathogenic infection, threats to human health due to the formation of disinfection byproducts (DBPs) may arise due to natural organic precursors. Regression-based models characterizing the formation of DBPs are derived from data for 28 conventional water treatment plants in Ontario. DBPs are shown to be correlated statistically with dissolved organic carbon, pre-and post-chlorination dosages, pH and temperature. Using backward elimination nonlinear regression, a set of mathematical functions are obtained (R2=0.62 to 0.79) for an array of DBPs. The models are used to guide decision-markers in the selection and operation of drinking water treatment processes to decrease DBP formation, indicating that a shift from emphasis on pre-chlorination to post-chlorination has the most effect on DBP formation.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".