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Accounting for Source Location on the Vulnerability Assessment of Water Distribution Network

2021· article· en· W3166341592 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.

Bibliographic record

VenueJournal of Infrastructure Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCentralityRanking (information retrieval)Robustness (evolution)Vulnerability (computing)Metric (unit)Computer scienceNetwork analysisGraph theoryGraphReliability engineeringData miningEngineeringEnvironmental scienceMathematicsStatisticsTheoretical computer scienceMachine learningComputer securityOperations management

Abstract

fetched live from OpenAlex

The vulnerability of water distribution networks (WDNs) to water-main breaks is used for prioritizing pipes in the network for maintenance planning. In this paper, a parameter of graph theory, algebraic connectivity (AC), is employed as a vulnerability metric for WDNs. The change in the magnitude of AC of the networks due to the removal of pipes was found to have a strong correlation with loss of robustness of the WDNs or the size of the network being isolated by the water-main breaks. However, because AC is a topographic measure of graphs, the effects of the location of water sources are not accounted for in ranking through the change in AC. A virtual network is proposed here to overcome the limitation and to move the centrality of the network to the pipes connecting to the water source. The resulting AC based ranking is found to have a correlation with hydraulic impact factor-based ranking of the pipes of WDNs.

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)0.001
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: none
Teacher disagreement score0.769
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.219
Teacher spread0.213 · 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