Infrastructure performance rating models for wastewater treatment plants
Bibliographic record
Abstract
Wastewater treatment plants (WTPs) are among the most complex municipal infrastructure systems that serve large populations. Unfortunately, many studies have shown that the WTPs, in the USA and Canada, are facing unprecedented deterioration due to ageing and improper maintenance plans. This situation is aggravated by the lack of adequate funds for upgrading and maintenance. In 2008, Statistics Canada estimated that WTPs exceeded 63% of their useful lives, the highest level among public infrastructure facilities. Similarly, the WTP performance in the USA had a near-failure average grade of D − . These facts show the urgent need for rehabilitation decision tools to keep these facilities running effectively. This research aims to respond to such a pressing need by developing a condition-rating index (CRI) model for the WTP infrastructure. The CRI is developed using an integrated approach of the analytical hierarchy process with the multi-attribute utility theory. The required data for these models are collected via questionnaires from site visits and interviews with experts in Canada and the USA. The results reveal that physical factors have the highest impact on deterioration of WTP infrastructure and that pumps are the most vulnerable infrastructure unit. The developed CRI workability is proved using data of three WTPs from Canada and the USA, which show robust results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".