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Record W4387039337 · doi:10.1049/icp.2023.0712

Fault localization system for medium voltage distribution underground network

2023· article· en· W4387039337 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIET conference proceedings. · 2023
Typearticle
Languageen
FieldEngineering
TopicSmart Grid and Power Systems
Canadian institutionsSaskatchewan Health Quality CouncilCentre for Research in Astrophysics of Québec
Fundersnot available
KeywordsFault (geology)VoltageComputer scienceFault indicatorReliability engineeringElectrical engineeringFault detection and isolationEngineeringGeologySeismologyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper addresses the topic of fault localization on medium voltage (MV) distribution underground feeders. Accurate fault localization is an important smart grid advanced distribution application related to network maintenance. The method and the offline underground fault localization system (UFLS), presented in this paper, are based on travelling waves theory. The paper discusses the concept and the design of an UFLS, developed by a CRHQ research team, and the results of the tests performed in laboratory or carried out on Hydro-Quebec's MV network.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.771

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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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