An evidential reasoning approach to evaluate intrusion vulnerability in distribution networks
Bibliographic record
Abstract
Intrusion, a primary mechanism of water quality failures in distribution networks, has accounted for approximately 15% of the total documented cases of waterborne illnesses in the Unites States in the last 30 years. Intrusion through water mains may occur during maintenance and repair events, through broken pipes and gaskets in the presence of contaminated soil and/or cross-connections. The potential of contamination through backflow or through leaky pipes increases whenever the water pressure in a pipe is very low or negative. This can occur when the pipe is de-pressurized for repair or when it is used to extinguish fire or during episodes of transient pressures. Intrusion of contaminants into water distribution networks requires the simultaneous occurrence of three elements; a contamination source, a pathway and a driving force. Each of these elements provides an independent body of evidence (typically incomplete and non-specific) which can give hint(s) of the occurrence of intrusion into distribution networks. Inference using traditional Bayesian analysis involves assumptions in case of incomplete information and partial ignorance. Evidential reasoning, also called Dempster-Shafer (DS) theory, has proved very useful in this situation and has the ability to incorporate both aleatory and epistemic uncertainties in the inference mechanism. The bodies of evidence from contamination source(s), intrusion pathway(s) and driving force(s) are mapped over a 'frame of discernment' of vulnerability of intrusion. Subsequently the DS rule of combination is applied to make an inference on the occurrence of intrusion. The implementation of the evidential reasoning method to assess vulnerability to intrusion in distribution networks is demonstrated with the help of an example.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".