Predicting trihalomethane formation in chlorinated waters using multivariate regression and neural networks
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Bibliographic record
Abstract
Research Article| May 01 2003 Predicting trihalomethane formation in chlorinated waters using multivariate regression and neural networks Manuel J. Rodriguez; Manuel J. Rodriguez 1Département d'Aménagement, 1624 F. A. Savard, Université Laval, Québec, QC, Canada, G1K 7P4 Tel: (418) 656-2131 ext. 8933 Fax: (418) 656-2018; E-mail: manuel.rodriguez@ame.ulaval.ca Search for other works by this author on: This Site PubMed Google Scholar Julie Milot; Julie Milot 2Centre de Recherche en Aménagement et Développement (CRAD), 1636 F. A. Savard, Université Laval, Québec, QC, Canada, G1K 7P4 Search for other works by this author on: This Site PubMed Google Scholar Jean-B. Sérodes Jean-B. Sérodes 3Département de Génie Civil, 1916 Pouliot, Université Laval, Québec, QC, Canada, G1K 7P4 Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2003) 52 (3): 199–215. https://doi.org/10.2166/aqua.2003.0020 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Twitter LinkedIn Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Manuel J. Rodriguez, Julie Milot, Jean-B. Sérodes; Predicting trihalomethane formation in chlorinated waters using multivariate regression and neural networks. Journal of Water Supply: Research and Technology-Aqua 1 May 2003; 52 (3): 199–215. doi: https://doi.org/10.2166/aqua.2003.0020 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Recently, there has been increased interest in modelling disinfection by-products (DBP) in order to better understand and manage the presence of these compounds in drinking water. In this paper, the use of artificial neural networks (ANN) to predict trihalomethane (THM) formation resulting from chlorination bench-scale experiments is investigated and compared with the use of classical multivariate linear regression (MLR). ANN and MLR were developed from three databases which were generated through bench-scale chlorination essays carried out in the US and Canada. A detailed analysis of modelling results shows that for all three databases, ANNs have in general a greater ability than MLRs to predict THM formation for most water quality and chlorination conditions, with the exception of instantaneous THMs (formation immediately following chlorine addition). chlorination, modelling, multivariate regression, neural networks, trihalomethanes This content is only available as a PDF. © IWA Publishing 2003 You do not currently have access to this content.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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