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Record W2037593855 · doi:10.1109/ceidp.2008.4772844

Influence of the Relative Amount of Paper on Aged Mineral Oil Decay Products

2008· article· en· W2037593855 on OpenAlexafffund
A. Bouaïcha, I. Fofana, M. Farzaneh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversité du Québec à Chicoutimi
FundersCanada Research Chairs
KeywordsPressboardMineral oilTurbidimetryMaterials scienceElectrical insulation paperFourier transform infrared spectroscopyEnvironmental scienceChemistryTransformerChemical engineeringEngineeringMetallurgyChromatographyElectrical engineering

Abstract

fetched live from OpenAlex

Aging of different oil-pressboard insulation ratios was investigated under controlled laboratory conditions. Modern diagnostic tools and techniques were used to assess the degree of influence of the relative amount of cellulosic materials on aged mineral oil decay products. Determination of relative content of dissolved decay products by spectrophotometry, solid suspension by turbidimetry, Fourier transform infrared spectroscopy, and electrical diagnostic techniques were used to judge the most affected oil parameters by oil/pressboard proportion and thus the most effective way to choose the best proportion that can reduce solid insulation degradation and increase power transformers lifespan. A quantitative study of the effect caused by the ratio of oil and paper can help to a better modeling of solid insulation end of life.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.192
Teacher spread0.184 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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