Contribution of Neural Networks for Modeling Trihalomethanes Occurrence in Drinking Water
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Bibliographic record
Abstract
The presence of chlorination by-products such as trihalomethanes (THMs) in drinking water has become an issue of particular concern for utility managers. Modeling THM occurrence in water may be a valuable tool for decision makers in dealing with these potentially hazardous by-products. This paper presents the application of artificial neural networks (ANNs) to model THM occurrence in drinking water. ANNs are compared with other modeling approaches, logistic regression and multivariate regression, to classify water utilities according to their susceptibility to generate high levels of THMs and to predict concentrations of formed THMs with variable water quality and chlorination conditions, respectively. In general, for both applications, ANN models gave similar or better results than other modeling techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it