Diminishing marginal returns for sensor networks in a water distribution system
Bibliographic record
Abstract
With increasing interest in the implementation/functionality of a contaminant warning system for water distribution systems, questions exist over the application to a real distribution system. A methodology is described to assess the impacts of changes in the numbers of sensors, on the time delay required to detect a contaminant intrusion event and to maximize sensor detection redundancy as protection against false positives. The methodology is used to explore the point of diminishing marginal return of detection likelihood, and the average time delay of detected intrusion events. Pareto front performance improvement with increasing numbers of sensors (from 2 through 50) is characterized through a case study application to the City of Guelph water distribution system (WDS). The results provide a methodology for utilities to employ for decisions on the number of sensors to use for a system. Within the two scenarios applied, five and four sensors are shown to be the point of diminishing marginal return for Guelph WDS in terms of the Pareto front performance improvement, detection likelihood, and the average time delay for the case study. Nevertheless, given that the timeframe to detect a contamination event may be lengthy, placing more sensors than the point of diminishing marginal return may be appropriate.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".