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Record W4385721135 · doi:10.1061/9780784485026.037

University of Alberta: Large Diameter Twin Raw Water Intake Pipeline Inspection, Condition Assessment, and Rehabilitation

2023· article· en· W4385721135 on OpenAlexaffabout
Olugbenga Samuel Ibikunle, Bola A. Kojeku

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPipeline (software)RehabilitationPipeline transportEnvironmental sciencePetroleum engineeringGeologyEngineeringMedicineEnvironmental engineeringPhysical therapyMechanical engineering

Abstract

fetched live from OpenAlex

The University of Alberta’s cooling plant (CP) uses the North Saskatchewan River (NSR) water to produce chilled water used to meet the university’s cooling needs. The river water is supplied to the plant via two concrete intake structures located on the bed of the river, each connected to a wet well by a “1980 built 54-in. diameter steel pipe,” which runs under the riverbed perpendicularly to the river flow. Inspection and condition assessment efforts completed in 2020 on the 320 ft long west intake pipe revealed significant tuberculation build-up, scale deposits, multiple pinhole, and weeping leaks. The rehabilitation program started with evaluation of the technical viability and overall suitability of various rehabilitation methodologies with consideration given to all applicable characteristics of the pipe and the anticipated service life extension. Composite liner system (combining CFRP and GFRP) emerged as the most technically viable option. The rehabilitation activities were completed successfully in September 2022 to design, specifications, schedule, and budget with no safety incident.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.648

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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 designNot applicable
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

Citations0
Published2023
Admission routes2
Has abstractyes

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