MétaCan
Menu
Back to cohort
Record W3209063681 · doi:10.1109/tdei.2021.009684

Tracking and Erosion of Silicone Rubber and EPDM Insulation in the DC Inclined Plane Test

2021· article· en· W3209063681 on OpenAlexaff
Refat Atef Ghunem, E.A. Cherney, Shesha H. Jayram

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of WaterlooNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceSilicone rubberComposite materialNatural rubberCombustionDepolymerizationCarbon blackAnodeVulcanizationForensic engineeringPolymer chemistryChemistryElectrode

Abstract

fetched live from OpenAlex

This paper investigates the tracking and erosion of silicone rubber and EPDM insulation in the DC inclined plane tracking and erosion test. Silicone rubber filled with alumina tri-hydrate fails due to combustion in the gas phase or erosion paths, but with no evidence of tracking. EPDM fails due to tracking and surface capturing fire, from dry-band arcing leaving a black residue while impinging the surface. Carbonaceous residue on EPDM gives rise to leakage current magnitude, whereas fused silica-based residue on silicone rubber is shown to promote combustion. A high amount of alumina tri-hydrate replacing the polymeric fuel in the composite is shown as a primary factor preventing failure and changing the nature of the residue in the DC inclined plane tracking and erosion test. Depolymerization is the main degradation mechanism reported for silicone rubber and EPDM, with notable degrees of crosslinking reported only for silicone rubber. Crosslinking reduces the amount of silicone rubber undergoing depolymerization, thus, the amount of fuel prone to combustion is reported to promote a coherent residue shield against the eroding dry-band arcing.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Dielectrics and Electrical InsulationSame topicElectrowetting and Microfluidic TechnologiesFrench-language works237,207