Contaminant intrusion in water distribution networks: review and proposal of an integrated model for decision making
Bibliographic record
Abstract
Contaminant intrusion in a distribution network (DN) refers to the entry of harmful chemicals and pathogens in the presence of three conditions: (i) the availability of a contaminant source near water mains; (ii) a pathway: leakage or breakage; and (iii) a driving force: low or negative pressure in the water main. The occurrence of contamination in a DN can take place frequently as there is no specific treatment at this stage except secondary disinfection. Contaminant intrusion requires as much attention as source water protection or treatment plants, particularly given that at this point, water is near the final stage prior to human consumption. Failure to detect and treat at this time could have potential negative impacts on consumers’ health. Following the September 11, 2001 attack, strict regulations are now enforced by the municipalities to monitor water quality within DNs. This review article focuses on various aspects of contaminant intrusion in DNs based on more than 90 journal articles, peer-reviewed conference proceedings, and research reports. Here we present details on the conditions of contaminant intrusion, water quality regulations, sampling, protection and mitigation strategies, and various modelling approaches for decision making. Based on this review, we propose an integrated model that will help guide effective decision making for contaminant detection and mitigation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".