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Record W2169101361 · doi:10.1109/mei.2005.1389268

Defect tolerance of solid dielectric transmission class cable

2005· article· en· W2169101361 on OpenAlexaff
Zhong Zheng, S.A. Boggs

Bibliographic record

VenueIEEE Electrical Insulation Magazine · 2005
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDielectricMaterials scienceConductorRange (aeronautics)Transmission (telecommunications)Dielectric strengthCatastrophic failureElectronic engineeringElectrical engineeringComposite materialOptoelectronicsEngineering

Abstract

fetched live from OpenAlex

This paper addresses the issue of determining the level of defect that is likely to cause the failure of solid dielectric transmission class cables. It also proposes methods for predicting the level of defect that is likely to cause failure and to provide a simple analytic approximation for doing so in the case of conducting spheroids aligned with the electric field. A common assumption is that conducting particles > 100 μm in length are likely to cause failure of extruded dielectric transmission cable. This analysis suggests that when the effects of operation at elevated temperature are included in the analysis, this is probably an appropriate criterion with a sound technical basis. For maximum background fields in the range of 15 kV/mm, as presently seen near the conductor shield of some transmission class cables, a worst-case particle length in the range of 0.1 mm is likely to be required to cause failure for the worst-case local polymer morphology in the range of the maximum operating temperature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations42
Published2005
Admission routes1
Has abstractyes

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