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Record W4392853296 · doi:10.1186/s40068-024-00334-x

Analysis of factors driving water main breaks across 13 Canadian utilities

2024· article· en· W4392853296 on OpenAlexafffundabout
Sadaf Gharaati, Rebecca Dziedzic

Bibliographic record

VenueENVIRONMENTAL SYSTEMS RESEARCH · 2024
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsConcordia University
FundersConcordia University
KeywordsEnvironmental scienceTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Deterioration of water infrastructure is a global challenge that jeopardizes water system ability to deliver water safely. While various factors affect watermain failure, previous studies have focused on common pipe attributes or general protection strategies. The main objective of this study is to examine the relationship between pipe break characteristics and system properties. Comprehensive data from thirteen Canadian water systems (over 60,000 failures) are examined with correlation and chi-squared analyses. Joint and fitting failures are most likely for pipes aged 20 years or less, and universal joints are most associated with joint failure. Pipes in clay and sand soils are more likely to break due to improper bedding and differential settlement, respectively. Furthermore, in the summer, accidental breaks of asbestos cement pipes are more likely, as are failures of pipes with collar joints and coal tar lined pipes. By exploring these relationships, the paper provides insights into opportunities for reducing water main failure, through improved design, maintenance and rehabilitation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.262
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes3
Has abstractyes

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