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Record W2105059868 · doi:10.1109/tpwrs.2013.2291446

Use of Mobile Unit Substations in Redundant Customer Delivery Systems

2014· article· en· W2105059868 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

VenueIEEE Transactions on Power Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsReliability engineeringTransformerProbabilistic logicMarkov chainComputer scienceMarkov modelMobile telephonyEngineeringOperations researchTelecommunicationsElectrical engineeringMobile radioArtificial intelligence

Abstract

fetched live from OpenAlex

A reliability study has been carried out recently to find out whether the use of a mobile unit substation (MUS) in redundant customer delivery systems is justified from the economic point of view when the station two companion transformers fail randomly. The transformer failures can be short or long depending on the nature of failure. The study uses a probabilistic method based on Markov models in assessing the benefit of MUS when the two transformers are not in service. The calculated benefit from the MUS is then compared with the cost of carrying the MUS to see whether the MUS is justified or not. The purpose of this paper is to describe the study assessment methodology, its assumption and the study findings.

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 categoriesMeta-epidemiology (narrow)
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 score0.756
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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