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Record W2030488178 · doi:10.1109/icpadm.2006.284271

Diagnostic Testing for Assessment of Distribution Cables

2006· article· en· W2030488178 on OpenAlexaff
M.A. Rahman, Prajna Ghosh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsBP (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Reliability engineeringEngineeringVoltageDirect-buried cableTask (project management)Computer scienceElectrical engineeringCable harnessSystems engineering

Abstract

fetched live from OpenAlex

Distribution network system owners and managers are increasingly more conscious on the significance of their ability to improve the condition and reliability of their network system. A combination of pertinent diagnostic testing are sought after for condition assessment. Adoption of the most appropriate diagnostic system between the one that are already developed and practiced and others that are promising but still in development is not an easy task to achieve. Installations of MVUG in TNB network system basically consist of PILC, XLPE, and mixed (PILC and XLPE) cables. The PILC cables were the first to be introduced. The XLPE cables came in the late 1980s. Currently only XLPE cables are used for new installation but limited length of PILC cables are still kept for maintenance purposes. The voltage levels for the underground cable network system are 11 kV, 22 kV, and 33 kV. At these voltage levels, the total lengths as of year 2004 are roughly 289,000 km for PILC cables and 257,000 km for XLPE cables. The performance of PILC cable is important in terms of supply reliability as its total length is still significant in the network. Therefore, monitoring the condition of these cables is considered very important and has to be looked into seriously

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.187

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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