Bibliographic record
Abstract
Evaluating the condition state of infrastructure assets is one of the most integral pieces of information to the asset manager. Water infrastructure poses specific challenges compared to sewer infrastructure where techniques like CCTV are now being consistently used to assess condition. The number of water main breaks is commonly used as a proxy for water main condition. Statistical water main performance models rely on using past breakage patterns and rates to predict future performance of the water main network. Performance models can be broadly classified into two groups. Rate-of-failure models attempt to extrapolate the breakage rate for a particular cohort of pipes and do not differentiate between the times between successive failures. Examples of statistical modeling approaches for these models include the Non-Homogeneous Poisson Process and the Multi-Variant Exponential (MVE) model. On the other hand, Transition-State (TS) models attempt to model the time between successive failures for pipes. Examples of these models include the Proportional Hazards Model and the Transition State - Life Regression Model. This paper presents a comparison and analysis of rate-of-failure models and transition-state models using a single data set for cast and ductile iron pipes in the City of Hamilton, Canada. The paper makes comparisons between the models' ability to support breakage forecasting, long-term strategic planning and short-term tactical planning. The paper also analyzes the data needs of each model and the impact of data inaccuracies (that are common in water main databases) on the predictive accuracy of each model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.001 |
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".