A fuzzy group decision-making model for Water Distribution Network rehabilitation
Bibliographic record
Abstract
Group Decision-Making (GDM), though could aid water companies in determining the Pipeline Rehabilitation Plans (PRPs) in any network size, can impose uncertainties on PRPs due to the different viewpoints of decision-makers. Thus, it should be determined when GDM could be ignored or should be considered for PRPs. To address this problem, a Fuzzy GDM model was developed to assess 10 scenarios of GDM in combination with 7 scenarios of network size. Hence, by participating experienced experts in different countries, PRPs were determined for various network sizes in a case study. The results indicated that the GDM could be ignored for PRPs with fewer pipes, whereas, should be considered for PRPs with more pipes. In addition, it was found that the groups with more decision-makers had less effect on pipes prioritization, but more influence on determining rehabilitation strategies. While, in groups with fewer decision-makers, the GDM could have the reverse effect.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".