Study of the Suitability of Existing Deterioration Models for Water Mains
Bibliographic record
Abstract
Deterioration of water mains causes serious problems in major urban communities worldwide. This paper presents the findings of a recent study conducted on pipe-break data in an effort to identify and categorize the key factors that contribute to the deterioration of water mains and examine the applicability of existing deterioration models for water mains in representing actual field conditions. The data used in this paper were collected from a municipality that has a large water distribution network in Canada. The collected data cover 15-year pipe break records of 432km of water mains. Unlike the common thought by practitioners, the performed analysis reveals that water mains of long lengths do not necessarily have more breaks compared to those of short lengths. The analysis performed using existing models shows that they provide poor representation and inadequate explanation for the deterioration of water mains. The research findings presented in this paper are expected to be useful to academics and practitioners (municipal engineers, consultants, and contractors) in analyzing deterioration trends of water mains.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".