Modeling the Disinfection of Waterborne Bacteria Using Neural Networks
Bibliographic record
Abstract
Neural networks offer an alternative approach to conventional mathematical models for modeling the disinfection of waterborne pathogens. The disinfection process was modeled using two different learning methods: back-propagation and simulated annealing. Simulated annealing is a robust method of optimization capable of escaping local optimums and determining global optimums. Gradient descent, which back-propagation is based on, is a more limited method of optimization that is unable to overcome local optimums. Many neural networks were developed using experimental data to model the disinfection of Escherichia coli and Eberthella typhosa using chlorine and chloramines. The neural network models were developed based on back propagation and simulated annealing and achieved comparable performance results. The models that were trained using simulated annealing required substantially more training time. Sensitivity analysis was used to explore the ability of the neural network models to learn known input variable trends for the disinfection process. Saliency analysis was used to rank the relative importance of each input variable. Each model successfully determined the appropriate input variable relationships. Based on the results of saliency analysis, all of the input variables were determined to be relevant to modeling the disinfection process for the studied combinations of disinfectants and pathogens. The disinfection model based on simulated annealing preformed slightly better relative to the model based on back propagation. Given the practical equivalence of performance results, the model based on back propagation is preferred as it avoids significant model training time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".