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Record W4254068755 · doi:10.18280/ejee.230202

Estimation Method of the State of 6-10 kV Distribution Network

2021· article· en· W4254068755 on OpenAlex
Saidjon Tavarov, А. И. Сидоров, Р. Г. Валеев, Ekatirina V. Zykina

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Industrial Safety
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Computer scienceReliability engineeringState (computer science)Electrical networkDistribution (mathematics)EstimationBasis (linear algebra)Electric power systemPower (physics)AlgorithmMathematicsElectrical engineeringPhysicsEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

The article analyzes the state of the elements of 6-10 kV distribution electrical networks in Dushanbe, showing the excess of reliability and efficiency indicators of the considered electrical networks from the permissible 10% values. For the first time, the factors influencing the reliability indicators of 6-10 kV electrical distribution network elements in Dushanbe were identified and a mathematical model of networks for determining the state of 6-10 kV electrical distribution network elements in Dushanbe was proposed. On the basis of them, an algorithm for monitoring the state of the elements of 6-10 kV electrical distribution networks in Dushanbe is proposed, which makes it possible to evaluate the efficiency of 6-10 kV distribution electrical networks in terms of undersupply of electricity - ΔW.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score0.203

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 head0.194
Teacher spread0.187 · 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