Exploration of the relationship between water main breaks and temperature covariates
Bibliographic record
Abstract
Water utilities (especially in colder climates) often experience an increase in water main breaks in colder seasons. Some observers argue that this increase largely occurs during the period when there are sudden and prolonged changes in water and air temperatures, which typically occur during the late fall to early winter (temperature drop) and late winter to early spring periods (temperature rise). This paper examines the impact of temperature changes on observed pipe breakage rate for three pipe materials, namely, cast iron, ductile iron and galvanised steel. Several water and air temperature-based covariates were tested in conjunction with a non-homogeneous Poisson pipe break model to assess their impact on water main breaks, using data sets from three different water utilities in the USA and Canada. Temperature-based covariates, such as average mean air temperature, maximum air temperature increase and decrease, and how fast the air temperature increase and decrease over a specific period of days, were found to be consistently significant. While the availability of water temperature data (which most utilities do not have) can enhanced the prediction of water main breaks, it appears that air temperature data alone (which most utilities can access) are usually sufficient.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".