Impacts of Water Quality on the Spatiotemporal Susceptibility of Water Distribution Systems
Bibliographic record
Abstract
Maintaining water quality in distribution systems is crucial for ensuring safety of water supply. The distribution infrastructure is typically buried underground and it is often difficult to assess the condition of the system. In case of breakage of underground pipe network, external agents, like microorganisms may ingress into the distribution system which then react with residual disinfectant (chlorine) and result in faster decay. Understanding the chlorine decay is critical to ensuring the acceptable condition of the water distribution infrastructure. The objective of this study is to assess the spatiotemporal susceptibility of a water distribution network based on water quality variations. GIS based residual chlorine decay, temperature, and Langelier saturation index (LSI) profiles for the entire water distribution network are created using inverse distance weighting technique. Hotspot analysis is conducted to identify the vulnerable sections of the city's water distribution network based on residual chlorine. Spatial variations can not identify a consistent pattern of decay throughout the distribution system. However, some sections of the old part (North western) of the city show low residual chlorine levels compared to other locations. LSI values are negative in some source waters, leading to potential degradation of the distribution pipes. Temporal variations indicate that residual chlorine levels drop late summer and early fall. However, hotspot analysis identifies that some of the old part of Sharjah has consistently low residual chlorine levels, indicating considerable degradation of the distribution pipes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".