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Record W2770135358 · doi:10.1109/tdei.2017.006792

On the experimental determination of the nitrogen content of thermally upgraded electrical papers

2017· article· en· W2770135358 on OpenAlexafffund
Esperanza Mariela Rodriguez-Celis, Jocelyn Jalbert, Steve Duchesne, O. H. Arroyo, Lidia B. Rodriguez, James G. Cross, Lance Lewand

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2017
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsKinectrics (Canada)Université du Québec à ChicoutimiHydro-Québec
FundersHydro-QuébecInstitut national de la recherche scientifique
KeywordsKjeldahl methodRepeatabilityNitrogenReproducibilityContext (archaeology)Content (measure theory)Characterization (materials science)Kraft paperCombustionAnalytical Chemistry (journal)Materials scienceMeasure (data warehouse)Process engineeringChemistryEngineeringComputer scienceNanotechnologyComposite materialEnvironmental chemistryMathematicsData mining

Abstract

fetched live from OpenAlex

In this paper, the nature of thermally upgraded Kraft electrical insulating papers and their characterization by measurement of nitrogen content is discussed. A historical context and explanation of the basics of the various chemical methods used to measure nitrogen content in electrical papers is presented. The Kjeldahl and Dumas methods for quantifying nitrogen content were compared using six insulating papers in different laboratories in an effort to correlate the results obtained with both techniques. The results provide an insight into the viability of the Dumas combustion method as a proposed industry standard test for measuring the nitrogen content of paper. Moreover, data on the accuracy, reproducibility, and repeatability of the Dumas method is provided for the analysis of thermally upgraded electrical papers.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.219
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.233
Teacher spread0.209 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

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