Methodology for Bayesian Belief Network Development to Facilitate Compliance with Water Quality Regulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Limited resources and drinking water quality requirements pose significant challenges to those managing small and rural drinking water distribution systems (WDSs). Real-time monitoring technologies could support regulatory compliance, if shortcomings such as false readings and data corruption could be overcome. Bayesian belief networks (BBNs) are proposed as a means to mitigate technological shortcomings and increase certainty about the state of a given WDS. This paper describes a methodology for the development of BBNs that integrates known system characteristics with real-time monitoring technologies to support the water quality compliance of small or rural WDSs. Expert judgment was used both in the development of the structure of the BBN and in quantifying the required probability relationships. The results of a case study application of this methodology suggest that it is useful in developing a BBN to support decision making for a WDS with limited use of real-time monitoring technology.
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.
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 | 0.000 |
| 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 it