Water quality failure in distribution networks: a framework for an aggregative risk analysis
Bibliographic record
Abstract
The pathways through which water quality can be compromized in the distribution system may be classified into five major categories: intrusion of contaminants into the distribution system, regrowth of bacteria in pipes and storage tanks, water treatment failure, leaching of chemicals or corrosion products from system components (pipes, storage tanks, liners, etc.) and permeation of organic compounds through plastic pipe and pipe components in the system. The characterization and quantification of these risk factors is complex and highly uncertain. The current inability to precisely quantify these risks may require the usage of a quantitative-qualitative framework. In this paper, a framework for aggregative risk analysis is proposed for water quality failure in the distribution system. Each basic risk item is expressed by a fuzzy numbers, which is derived from the product of the likelihood of a failure event and its consequence. The fuzzy numbers capture vagueness inherent in the qualitative (linguistic) definitions. A multi-stage hierarchical model for water quality failure is developed. An analytic hierarchy process is used for estimating the weighting scheme for grouping risk items. The framework is demonstrated with a simplified structure of risk hierarchies for water quality in distribution systems.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".