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Record W2902779323 · doi:10.1139/cjce-2017-0725

A streamlined energy efficiency performance indicator for water distribution systems: a case study

2018· article· en· W2902779323 on OpenAlexaffvenue
Brett Snider, Yves Filion

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsEnergy performanceEnergy (signal processing)Efficient energy useComputer sciencePerformance indicatorReliability engineeringEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

This note investigates the validity of the energy efficiency performance indicator developed by Snider and Filion for water distribution systems. This performance indicator estimates the minimum mechanical energy required in a distribution system by assessing the unavoidable energy losses due to friction, leakage, and user demand. The performance indicator provides a simplified method to calculate the energy efficiency of a water distribution system without the application of a hydraulic simulation model. To validate the proposed streamlined approach, this note calculates the energy indicator using a hydraulic model and extended period simulation for a number of synthetic and real water distribution system models. A Pearson correlation coefficient of 0.958 for the overall mechanical energy efficiency indicator was calculated between the two approaches (streamlined and extended period simulation). This suggests the simpler streamlined approach may be used instead of the more complicated extended period simulation approach.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.979
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.170
Teacher spread0.164 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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