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Record W1967011044 · doi:10.5942/jawwa.2013.105.0157

Life‐cycle energy analysis of performance‐ versus age‐based pipe replacement schedules

2013· article· en· W1967011044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Water Works Association · 2013
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPayback periodEmbodied energyLeakage (economics)Environmental scienceEnergy consumptionEnergy (signal processing)Operations managementEngineeringReliability engineeringProduction (economics)EconomicsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Although many North American water utilities are upgrading their systems through both regular maintenance programs and additional pipe replacement, rarely does this process take whole‐of‐life considerations into account. This article details the development of a life‐cycle energy analysis that accounts for energy associated with reducing leakage through pipe replacement and describes its implementation at a large water distribution system. Energy used in pumping was compared with the embodied energy tied to pipe replacement in a baseline scenario and three replacement plans. Results indicated that the annual operational energy savings of 4.9 × 10 to 6.4 × 10 kW·h achieved by 2020 comes at a cost. The embodied energy invested in replacing pipe stock for ductile‐iron pipes with diameters of 6 to 16 in. would be 0.88 × 10 to 2.05 × 10 kW·h/mi, a significant expense that results in an initial energy payback period of 17.6 years for the most aggressive replacement plan.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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