Comparison of four models to rank failure likelihood of individual pipes
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
The use of statistical methods to discern patterns of historical breakage rates and use them to predict water main breaks has been widely documented. Particularly challenging is the prediction of breaks in individual pipes, due to the natural variations that exist in all the factors that affect their deterioration and subsequent failure. This paper describes alternative models developed into operational tools that can assist network owners and planners to identify individual mains for renewal in their water distribution networks. Four models were developed and compared: a heuristic model, a naïve Bayesian classification model, a model based on logistic regression and finally a probabilistic model based on the non-homogeneous Poisson process (NHPP). These models rank individual water mains in terms of their anticipated breakage frequency, while considering both static (e.g. pipe material, diameter, vintage, surrounding soil, etc.) and dynamic (e.g. climate, operations, cathodic protection, etc.) effects influencing pipe deterioration rates.
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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