MétaCan
Menu
Back to cohort
Record W3215598383 · doi:10.1002/9781119800194.ch5

End Life Behavior of Ester Liquids in High‐Voltage Transformers

2021· other· en· W3215598383 on OpenAlexaff
U. Mohan Rao, I. Fofana, L. Loiselle, T. Jayasree

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsColloidColloidal particleChemical engineeringCelluloseFiltration (mathematics)Degradation (telecommunications)ChemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In this chapter, behavior of ester liquids are reported with an aim to understand the generation of decay particle (colloidal and soluble/dissolved particles) with aging. Thermal aging of oils is carried out as per ASTM D1934 in presence of cellulose and copper with aging history at elevated temperatures followed by analysis of colloidal and soluble particles. The aged and treated oils are characterized using diagnostic characterizations. The evolution and influence of colloidal and soluble decay contents for esters fluids and mineral oils are reported. The effect of colloidal particles on degradation of ester liquids is negligible, while degradation in mineral insulating oils is majorly governed by the colloidal particles. In addition, the potential of fuller's earth filtration for ester liquids is reported at different concentrations and different testing temperatures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.216
Teacher spread0.207 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicPower Transformer Diagnostics and InsulationFrench-language works237,207