Full-scale determination of pipe wall and bulk chlorine degradation coefficients for different pipe categories
Bibliographic record
Abstract
Abstract Having good information about parameters that impact water quality can improve the management of water distribution systems in the short-term (optimising disinfection) and the long-term (planning rehabilitation). Full-scale data on the degradation of the residual disinfectant for various pipe characteristics are difficult to obtain but necessary. As the most common disinfectant is chlorine, this paper aims to determine the most important pipe and/or hydraulic system characteristics in the chlorine degradation coefficients. Such characteristics were identified based on statistical analyses that relate them with range values of bulk and pipe wall degradation coefficients estimated in full-scale conditions in a real distribution system. The results showed that among pipe characteristics, the period of installation impacts significantly kw and kt. Results of kw for three different materials confirmed that residual chlorine degradation at the pipe walls for grey-cast iron, which is older and metallic, is much higher than that for ductile cast iron and PVC pipes. In older pipes, up to 97% of residual chlorine can be degraded at the pipe walls, while the role of bulk reactions can reach about 35% in newer pipes. The obtained information can be integrated to identify pipes for rehabilitation/renewal and locations for booster rechlorination.
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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 | 0.001 | 0.001 |
| 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".