Application of back-propagation neural network modeling for free residual chlorine, total trihalomethanes and trihalomethanes speciation
Why this work is in the frame
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Bibliographic record
Abstract
The application of back-propagation neural networks (BPNNs) was assessed for modeling residual chlorine decay, total THM concentrations (TTHMs), and three individual THM species (CHCl 3 , CHBrCl 2 , and CHBr 2 Cl) in water that was chlorinated under laboratory-scale conditions. Data for modeling chlorine decay and TTHM were generated in chlorination experiments carried out with water collected in water utilities of the Quebec City region, whereas data for THM species were provided by the US Geological Survey for the Mississippi River and its tributaries. The BPNN models were compared with conventional models developed with exactly the same data. Results showed that the ability of BPNN to model residual chlorine decay is, in general terms, comparable to the ability of kinetic first- and second-order models. For TTHMs and THM speciation, however, the ability of BPNN was clearly higher in comparison with multivariate regression models, in particular when brominated disinfection by-products (DBPs) (CHBrCl 2 and CHBr 2 Cl) were modeled. The successful application of BPNN presented in this study opens the door to other potential applications of BPNN for field-scale data concerning THMs as well as for other relevant disinfection by-products. Key words: back-propagation neural networks, drinking water, chlorination, residual chlorine decay, trihalomethanes, prediction.
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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