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Record W1506986020 · doi:10.1109/ceidp.1989.69531

Determination of gases and gas pressure in GIS spacer voids

2003· article· en· W1506986020 on OpenAlexaff
J.M. Braun, J.H. Groeger

Bibliographic record

VenueConference on Electrical Insulation and Dielectric Phenomena · 2003
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsEpoxyCuring (chemistry)NitrogenGas chromatographyOxygenMaterials scienceSwitchgearComposite materialNitrogen gasChemical engineeringChemistryAnalytical Chemistry (journal)Environmental chemistryOrganic chemistryChromatographyMechanical engineering

Abstract

fetched live from OpenAlex

The gas contents of voids found in commercial epoxy spacers and laboratory samples are investigated as part of a broader investigation into the aging characteristics of GIS (gas-insulated switchgear) spacers. Gas chromatography techniques were successful in identifying permanent gases and organic vapors in epoxy spacers. Simple crushing and, where practical, drilling were used to release the gaseous effluents. Analyses performed on production epoxy spacers yielded predominantly nitrogen and oxygen with, in much smaller concentrations, uncured residues and curing by-products similar to those dissolved in the epoxy mass. Given the high temperatures at the time of formation of the cavities, diffusion processes in the liquid-like mass will establish rapid equilibrium between the voids and the surrounding epoxy mass. Control of the gas and pressure content in epoxy cavities is similarly difficult to achieve where desired because of diffusion effects in the curing mass. While oxygen depletion can be readily ascribed to reaction with the uncured epoxy, the presence of excess nitrogen cannot yet be explained satisfactorily.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.261
Teacher spread0.237 · 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 designTheoretical or conceptual
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

Citations19
Published2003
Admission routes1
Has abstractyes

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