Integrated performance assessment model for water distribution networks
Bibliographic record
Abstract
The American infrastructure report card in 2013 rated the US water system infrastructure with grade of ‘D’. The Canadian infrastructure report card in 2012 stated that around 15.4% of Canada’s water infrastructure has a condition of fair to very poor. Thus, there is a critical need to develop efficient inspection, maintenance and rehabilitation plans for water distribution networks. However, such plans require an assessment tool to evaluate the performance and condition of water distribution networks. Therefore, the main purpose of this paper is to develop an integrated performance assessment model for water distribution networks. Two modules were developed to assess the performance of water pipelines and accessories, respectively. A third module was developed to assess the performance of water segments that includes pipelines and accessories. Moreover, a fourth module was built to assess the performance of each sub-network and the entire network based on the segments’ connection type using a reliability-based approach. To assess the performance of the water distribution network, the critical factors affecting its pipelines and accessories were identified and studied. The fuzzy analytic network process technique was used to obtain the importance weights of the identified factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".