Water Main Break Rates in the USA and Canada: A Comprehensive Study
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
Municipalities and the people they serve depend on pipe networks that provide safe drinking water. This piping is underground, out of sight, and often neglected. Overall assessment of water infrastructure condition is not good. Using the US as an example: In 2009, the American Society of Civil Engineers (ASCE) issued a US report card and gave a D- to drinking water infrastructure. In 2017, the grade improved to a D. In 2021, the grade was raised to a C-, better but still not good. Utilities are currently losing 11% of their water to leakage. Pipe life estimates of 75 to 100 years contrast with an average replacement schedule of about 200 years (ASCE, 2017). The American Water Works Association (AWWA) has also reported on water main replacements in the US. In the annual AWWA State of the Water Industry Report, renewal and replacement of aging water and wastewater infrastructure was listed as the top concern (AWWA, 2017). This has remained a primary issue for utilities nationwide for the last five years (AWWA, 2023). Deteriorating water mains are threats to the physical integrity of distribution systems, causing adverse effects on flow capacity, system pressure, and water quality (Grigg, et al., 2017). In addition to maintenance requirements and economic impacts, consequences of a broken water main include local flooding, interruption of water delivery, and damage to roads and private property. These outcomes also negatively affect a utility's customer satisfaction. Utility data clearly indicate that the integrity of water pipelines in the US and Canada continues to deteriorate as the infrastructure ages. Among the many indicators of aging pipes, break rates are the most significant.
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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