Bibliographic record
Abstract
The statistical analysis of historical breakage patterns of water mains is a cost effective approach to discern their deterioration, where physical mechanisms that lead to their deterioration are often very complex and not well understood. Furthermore, data required to model these physical mechanisms are rarely available and prohibitively costly to acquire. Several models exist in the literature, which use various statistical methods to analyse patterns of pipe breakage histories. Some of these models were designed to address relatively large groups of pipes, which are presumed to be homogeneous with respect to their deterioration patterns, while others address individual water mains. However, predicting a breakage pattern in an individual pipe has proven to be quite a challenge and the validation of these models is generally done on the basis of aggregate breakage rate although the model purports to predict individual pipe behaviour. The structural deterioration of water mains and their subsequent failure are affected by many factors, both static (e.g., pipe material, pipe size, age (vintage), soil type) and dynamic (e.g., climate, cathodic protection, pressure zone changes). Dynamic factors can currently be considered only in a model that was designed to deal with pipe groups. While group deterioration analysis is important for high-level renewal planning, operational considerations require the prioritisation of individual pipe for renewal within such groups. Consequently, the National Research Council of Canada (NRC), with support from the American Water Works Association Research Foundation (AwwaRF) is investigating how to prioritise individual pipes within a so-called `homogeneous' group of water mains. Several approaches have been explored in this research initiative with various degrees of success. In this paper we describe the development of a non-homogeneous Poisson model, which considers dynamic factors that can affect water main failure and some preliminary results are reported.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".