Prioritizing Pit Cast Iron Small Diameter Watermains for Assessment
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
Watermains distribution systems are critical assets that are prone to deterioration due to aging and other influencing factors. Although periodic inspections are needed, the physical assessment may involve extensive labor and financial burden on pipe owners. Therefore, it is paramount to prioritize watermains for assessment. While the risk-based approach is commonly used approach to prioritize watermains for assessment, the initial probability of failure is usually based on expert’s judgment and/or a hazard function that is based mainly on watermains breakage records. For those pipe owners with limited pipe breakage history records, they rely more on expert’s judgment. This paper presents a probabilistic failure model to prioritize pit cast iron pipes for assessment under combined internal pressure and external loading. While the corrosion pits that form during the process of graphitization is simulated using a power model, the in-service strength degradation is accounted for using the Weibull extreme value probability distribution. Uncertainty in the model was addressed using Monte Carlo Simulation.
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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