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Record W2057378733 · doi:10.1109/tdei.2013.6508773

New methods of DGA diagnosis using IEC TC 10 and related databases Part 1: application of gas-ratio combinations

2013· article· en· W2057378733 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2013
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsAcetyleneMethaneEthyleneMethane gasGas analysisDissolved gas analysisGas chromatographyComputer scienceDatabaseMaterials scienceData miningProcess engineeringChemistryEngineeringChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

This paper proposes a new method of DGA diagnosis, using IEC TC 10 and related databases. We analyzed five gas components (hydrogen, methane, acetylene, ethylene and ethane), which are used for analysis in the standards, with 10 types of gas ratios, and came up with 6 types of gas ratios among them, that can classify faults. Then, we reorganized and analyzed the 6 gas ratios with 15 gas-ratio combinations. From the results, we can suggest a new diagnosis method using 3 gas-ratio combinations that are able to classify fault areas clearly.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.276
Teacher spread0.253 · 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