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Record W2799007860 · doi:10.1109/tdei.2018.007200

Methanol in oil interpretation model based on transformer post-mortem paper analysis

2018· article· en· W2799007860 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 · 2018
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsTransformerMethanolReliability engineeringEngineeringComputer scienceProcess engineeringMaterials scienceElectrical engineeringChemistryVoltageOrganic chemistry

Abstract

fetched live from OpenAlex

In the last decade, much effort has been invested in using methanol as an oil-soluble chemical marker for assessing the condition of insulating paper. The use of this marker as paper life index presents many advantages particularly with new transformers insulated with thermally upgraded papers. However, until now, no interpretation model has been available for its extensive use by the transformer community. In this paper, a methanol-based interpretation model is presented for the first time using postmortem paper analysis on core-type transformers. This model allows the evaluation of the average degree of polymerization of a transformer's cellulose winding. Furthermore, threshold values based on the methanol concentration are given using this approach. Finally, model validation was performed on a limited number of papers taken from transformers being assessed in the repair shop.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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