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Record W2563665997 · doi:10.5942/jawwa.2017.109.0001

Impact of Urban Development on Energy Use in a Distribution System

2016· article· en· W2563665997 on OpenAlexafffund
Hannah G. Wong, Vanessa Speight, Yves Filion

Bibliographic record

VenueAmerican Water Works Association · 2016
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's UniversityKingston Health Sciences Centre
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceLift (data mining)Energy (signal processing)Enhanced Data Rates for GSM EvolutionEnergy systemEnergy sourceEnergy distributionRenewable energyComputer scienceEngineeringMathematicsTelecommunicationsStatisticsPhysics

Abstract

fetched live from OpenAlex

This research examined the effect of urban development patterns on the energy use of a complex water distribution system in the United States. The energy inputs and outputs to each pressure zone were quantified under historical expansion and urban intensification scenarios. Results showed that the lift energy increased in zones farthest from the river source. Frictional energy loss was highest in the zone nearest the source because system pipes conveyed flows to meet demands in all zones. While the zone nearest the source had a higher connectivity, the frictional energy loss in this zone was comparable to that in zones near the edge of the system with a lower level of connectivity. In the intensification scenario, the reallocation of demand from the edge to the urban core of the system decreased energy use by 50%, reduced frictional losses from 22 to 16%, and increased the energy supplied to users.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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 teacher head, 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

Citations8
Published2016
Admission routes2
Has abstractyes

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