Bibliographic record
Abstract
Water network performance assessment is a challenging concern that is facing worldwide municipalities. The necessity of providing continuous potable water under tight budget places extra pressure on municipalities and triggers the need for proper performance assessment. Accordingly, this research opts to develop a water networks performance assessment (WNPA) model to precisely assess the performance of the water networks’ components. The model revolves through two key indices: (1) Pipes Performance Index (PPI) and (2) accessories performance index (API). These indictors reflect the status of network components, their deterioration levels and propose consequence preventative actions. Furthermore, WNPA utilized a fuzzy analytical network process (FANP) to identify and evaluate the weights of functional performance criteria (physical, operational, quality of service, and environmental) for the pipes and accessories. It also exploits both the preference ranking organization method of enrichment evaluation (PROMETHEE) and simple multi attribute utility theory (MAUT) to compute the functional and global performance indices for the network components. In order to compute the weights, data are collected through water network experts. WNPA is applied to a Canadian water sub-network in which the results showed that most of existing pipes and accessories are in a medium state, which is well-aligned with the actual results. Thus, it can be concluded that WNPA proved to be a promising tool with high capability in assessing the performance of water networks’ components.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".