Modeling of the Chlorine Content Degradation in a Network of Drinking Water
Bibliographic record
Abstract
In an urban water supply network, the chlorine is used as a disinfectant. Its absence involves the risk of microbial contamination which can degrade the quality of water. According to the standards of World Health Organization, the residual chlorine content in water shouldn’t be lower than 0.25 mg/l. Chlorine reacts with many compounds and thus undergoes degradation throughout its transit in the conduct of the network. This degradation is the result of several physical factors (age of conduct, matter, flow, speed…) and bacteriological (germs, coliform, biofilm…). In this paper, we use a mathematical model for mapping the degradation of the Chlorine along the water supply network. The concentration of chlorine in each control is also performed. We used an algorithm of Hierarchical Classification taking into account some characteristics (nature of conduct, speed, flow). Results shows that degradation of Chlorine calculated matched well with the measured one. This model allowed us to have a real-time data about the Chlorine content and thus to overcome the problem of the measure of the chlorine due to the non accessibility of the conducts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".