Modeling of the Chlorine Content Degradation in a Network of Drinking Water
Bibliographic record
Abstract
<span lang="EN-US">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.</span>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".