Study on relationships between climate-related covariates and pipe bursts using evolutionary-based modelling
Bibliographic record
Abstract
Researchers extensively studied external loads since they are widely recognized as significant contributors to water pipe failures. Physical phenomena that affect pipe bursts, such as pipe-environment interactions, are very complex and only partially understood. This paper analyses the possible link between pipe bursts and climate-related factors. Many water utilities observed consistent occurrence of peaks in pipe bursts in some periods of the year, during winter or summer. The paper investigates the relationships between climate data (i.e., temperature and precipitation-related covariates) and pipe bursts recorded during a 24-year period in Scarborough (Ontario, Canada). The Evolutionary Polynomial Regression modelling paradigm is used here. This approach is broader than statistical modelling, implementing a multi-modelling approach, where a multi-objective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions vs. fitting to data. The analyses yielded interesting results, in particular for cold seasons, where the discerned models show good accuracy and the most influential explanatory variables are clearly identified. The models discerned for warm seasons show lower accuracy, possibly implying that the overall phenomena that underlay the generation of pipe bursts during warm seasons cannot be thoroughly explained by the available climate-related covariates.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".