Forensics of water quality failure in distribution systems - a conceptual framework
Bibliographic record
Abstract
Precise causes of water quality failures are often difficult to pinpoint. The complexity of the distribution system (many kilometers of pipes of different materials and ages), occurrences of physical/chemical/biological processes and the lack or absence of timely data make forensic analyses of water quality failure events very challenging. Water quality failure in the distribution system can occur through several pathways. These include intrusion of contaminants through failed or compromised pipes and cross-connections, regrowth of microbes in pipes and distribution storage tanks, leaching of chemicals or corrosion products from system components (pipes, tanks, liners), water treatment failure, deliberate contamination by terrorists and permeation of organic compounds through various plastic components of the system. Various indicators of water quality failure (symptoms) such as changes in the turbidity, odour, taste and colour, waterborne illnesses ranging from minor to serious, etc. can provide clues as to the causes of the failure in much the same way as symptoms of human health are used to diagnose causes and propose treatment.
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 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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".