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Record W1848789383 · doi:10.1109/elinsl.1990.109743

Performance of RTV silicone rubber insulator coatings

2002· article· en· W1848789383 on OpenAlexaff
S.H. Kim, E.A. Cherney, R. Hackam

Bibliographic record

VenueIEEE International Symposium on Electrical Insulation · 2002
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceSilicone rubberComposite materialVulcanizationArc flashInsulator (electricity)Leakage (economics)Natural rubberContact angleCoatingConductivityElectric arcSiliconeElectrode

Abstract

fetched live from OpenAlex

The results of the performance of room-temperature vulcanizing (RTV) silicone rubber insulator coatings in a salt-fog chamber are presented. The coatings were evaluated at 900- mu S/cm conductivity for the water forming the salt-fog. The loss of hydrophobicity was studied from monitoring the leakage current on the coatings. The effect of scale formed during dry band arcing was evaluated with respect to the performance of RTV coatings. The development of leakage current on the RTV coatings without scale is associated with a temporary loss of surface hydrophobicity. However, with scale deposited during dry band arcing the leakage current development is not only dependent on the temporary loss of hydrophobicity, but also strongly dependent on the amount of scale. The flashover voltage of the RTV coatings was determined subsequent to varying the time of the application of the electric stress in the salt fog. The performance of RTV coatings was studied as a function of the alumina trihydrate (ATH) filler level. Leakage current and contact angle measurements were used as indicators of the effectiveness of the RTV coatings. RTV coatings enable insulation systems to attain increased flashover performance by about 30% over uncoated insulators.>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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 designObservational
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

Citations10
Published2002
Admission routes1
Has abstractyes

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