A fuzzy rule-based approach for water quality assessment in the distribution network
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
In this paper, a fuzzy rule-based system with final evidential aggregation is proposed to perform the relative quality assessment of drinking water in the water distribution network (WDN). Partially reliable sensor measurements, incomplete assessments as well as subjective information on water quality parameters (WQP) introduce uncertainty to the water quality assessment process. Historical data recorded in a network are categorized into two groups including microbial and physicochemical parameters. Then, separate rule bases are developed to define microbial and physicochemical aspects of water quality. The distributed assessments of the water quality that result from two rule bases are aggregated using a fuzzy evidential reasoning algorithm. The proposed inference engine provides a decision support tool, which aids the decision makers to come up with management policies based on hundreds of water quality monitoring records. Statistical data on WQPs at fifty-two sampling locations of Quebec City main WDN were used to test the performance of the proposed framework.
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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