Validation of water main failure predictions: A <scp>2‐year</scp> case study
Bibliographic record
Abstract
Abstract Recent studies have shown that U.S. water mains are failing at an accelerating rate. In the meantime, water utilities are challenged by limited funding. It is important that water mains with much higher likelihood of failure (LOF) are replaced before they fail to avoid possible high consequences, such as public safety threats, high financial losses, and environmental damages. This article presents a model to evaluate the LOF of water mains using data available in geographic information systems (GIS). A case study is presented comparing 2 years of actual water main break data with the results of the model. The comparison shows a strong correlation between the model prediction and the actual break rates of main pipes; thus, it validates the robustness of the model and shows that funding can be used more efficiently by focusing on the water mains with a high LOF as predicted by the GIS model. This model has been used in New Jersey American Water's distribution systems. It can be used in other water systems to help guide water main replacement efforts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".