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Record W2067913901 · doi:10.1109/ceidp.2008.4772836

Measurement of the Evolution of Dripping Water Conductivity of an Ice-covered Insulator During a Melting Period

2008· article· en· W2067913901 on OpenAlexafffund
F. Meghnefi, M. Farzaneh, C. Volat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversité du Québec à Chicoutimi
KeywordsArc flashConductivityInsulator (electricity)Materials scienceComposite materialVoltageElectrical resistivity and conductivitySurface conductivityLiquid waterEnvironmental scienceElectrical engineeringGeologyChemistryEngineering

Abstract

fetched live from OpenAlex

This paper deals with the measurement of the evolution of the water conductivity dripping from an ice-covered insulator during a melting period. For this purpose, post type insulator covered by a wet-formed ice deposit was submitted to melting period tests leading to flashover. During each melting period, leakage current, applied voltage and dripping water conductivity were recorded simultaneously. To measure with precision the conductivity of dripping water, a system of measurement in real time based on a Plexiglas cell was developed. It was demonstrated that dripping water conductivity reaches its maximum value before the occurrence of flashover. For each flashover test performed under the same experimental conditions, the conductivity of the dripping water presents a similar evolution. These results will be helpful to understand the dynamic behavior of ice-covered insulator flashover thus contributing to increase the reliability of flashover numerical models.

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)0.000
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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.024
GPT teacher head0.219
Teacher spread0.195 · 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

Citations8
Published2008
Admission routes2
Has abstractyes

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