Infrastructure performance rating models for wastewater treatment plants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it