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Record W2168997797 · doi:10.1061/9780784413692.025

Predicting the Remaining Life of Asbestos Cement Pipe with Acoustic Wall Thickness Testing

2014· article· en· W2168997797 on OpenAlexaff
Gregory K. Robbins, Dave Johnston, Kevin Laven

Bibliographic record

VenuePipelines 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsHydrogenics (Canada)
Fundersnot available
KeywordsAsbestos cementCementService lifePrioritizationMains electricityForensic engineeringWater pipeService (business)EngineeringEnvironmental scienceAsbestosMaterials scienceReliability engineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Introduced as a building material in the mid-1900s, asbestos cement pipe makes up approximately 18% of all water mains in North America. Although its use declined in the 1980s after health concerns began to arise, a large amount of asbestos cement water pipe remains in service in many municipalities. While many have chosen to remove all asbestos cement mains from service, the combination of budget constraints and the complexity of its removal means the remaining pipes must be systematically prioritized. Remaining service life calculations based on the physical condition of the pipe are of significant value in this prioritization process. Prediction of pipe failure involves three core components: assessing the main's current physical condition, predicting how the condition will change, and determining the condition in which it will fail. This paper presents a simple yet effective method specific to asbestos cement pipe, with a focus on structural failure of the pipe wall.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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
Published2014
Admission routes1
Has abstractyes

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