Prediction Models for Annual Break Rates of Water Mains
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
Annual break rates are often used by municipalities as one of the most important criteria in rating the condition of water mains. This paper presents the development of deterioration models that predict the annual break rates of water mains considering pipe material, diameter, age, and length. The data used in this paper are collected from a Canadian municipality that has a large water distribution network. The collected data cover 15-year pipe break records of 432km of water mains. Five multiple regression models are developed, which show robust statistical analysis. Twenty percent of break data were randomly selected for validation in which the developed models demonstrate satisfactory results. The research presented in this paper is expected to be useful to academics and practitioners (municipal engineers, consultants, and contractors) in analyzing deterioration trends of water mains.
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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 | 0.001 |
| 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