Systems analysis models for disinfection by-product formation in chlorinated drinking water in Ontario
Bibliographic record
Abstract
Results of examination of the formation and control of disinfection by-products (DBPs), specifically total trihalomethanes (TTHMs) and total haloacetic acids (HAAs) in water treatment, are described in this article. Systems analysis models for TTHMs and HAAs for drinking water treatment plants using 28 surface water sources in Ontario are developed. Statistically, significant predictive regression models for TTHMs from dissolved organic carbon (DOC), chlorination and temperature (r 2=0.72) and HAAs from DOC, chlorination and pH (r 2=0.72) are demonstrated. These models are used to consider options to decrease DBP formation by shifting from pre-chlorination to post-chlorination, demonstrating that the potential may exist by applying more of the chlorine at a later point in the treatment sequence. This type of shift may reduce TTHMs by up to 63% and HAAs by up to 39% for the conditions being experienced at Ontario surface water treatment plants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".