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Mechanical and Hydraulic Reliability Estimators for Water Distribution Systems

2019· article· en· W2974813952 on OpenAlexaff
D. Páez, Yves Filion

Bibliographic record

VenueJournal of Water Resources Planning and Management · 2019
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsEstimatorReliability (semiconductor)Benchmark (surveying)Reliability engineeringEntropy (arrow of time)Computer scienceHydraulic machineryMathematical optimizationMathematicsStatisticsEngineering

Abstract

fetched live from OpenAlex

Reliability of water distribution systems is typically divided into mechanical and hydraulic reliability. This paper introduces two reliability surrogate measures: the mechanical reliability estimator and the hydraulic reliability estimator. These estimators were developed based on simplifications of a broader definition of stochastic reliability and were tested in the multiobjective design optimization of the Hanoi and Fossolo benchmark networks. The numerical values of the estimators were compared with existing reliability surrogate measures (the residence index, the network resilience index, and flow entropy) through correlation analysis. Results showed that the proposed estimators concur with existing optimization algorithms. They also show that previous indexes correlate with at most one of the types of reliability, while a trade-off between them seems to appear, especially in one of the study cases.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.227

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 designSimulation or modeling
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

Citations12
Published2019
Admission routes1
Has abstractyes

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