Bibliographic record
Abstract
Municipalities in Canada have noted that 59% of their water systems need repair, and the condition of 43% of those systems is apparently intolerable. Hence, large volumes of rehabilitation projects are necessary to improve the performance of Canadian water systems. A reliability (R) and criticality index (CI) are crucial for prioritizing rehabilitation activities and distribute budgets efficiently. “R”is a function of failure rate, while“CI”is a function of economic, operational, social, and environmental factors. The research presented in this paper aimed to develop a performance index (PI) that considered the combination of “R” and “CI” for water networks. Sound techniques were utilized to generate the PImathematical model, such as reliability theory, simple multi-attribute rating technique (SMART), and Analytical Hierarchy Process (AHP). Data on water networks were collected from the city of Hamilton, Ontario, Canada. Four sub-networks were selected from the entire network to represent four types of land uses; undeveloped, residential, park, and commercial/industrial. The results showed that the PI value was 0.72, 0.93, 0.87, and 0.76 for these types of land uses, respectively. The developed models and tools are assumed to be beneficial for municipal engineers and managers as well as academics.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".