Assessment of Mitigation Strategies for Disinfection By-Product Formation through Bayesian Network Modeling
Bibliographic record
Abstract
Issues of disinfection by-product (DBP) formation at water treatment plants (WTP) in response to chlorination in drinking water treatment systems are common.Controlling the rate of DBP formation is complicated by the presence of zebra mussels, which may inhabit the raw water intake of WTPs.Chlorination to control zebra mussel populations may exacerbate the formation of DBPs.A Bayesian network is developed using the WEBWEAVR-IV Toolkit, utilizing causal relationships between raw water quality parameters in the form of conditional probabilities.Three alternative chlorination scenarios are analyzed, one of which demonstrates the probability of high total trihalomethanes (TTHM) (>0.08 mg/L) can reduce the health risk by 6% to 9%.Given the input parameters of an average WTP, raw water concentrations from the data from 2005 to 2008, the health risk has a 14% chance of being less than 'de minimus' (one in a million).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".