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Record W4242054248 · doi:10.1109/tpel.2014.2302329

IEEE Power Electronics Society Information

2014· article· en· W4242054248 on OpenAlexaff
Jinjun Liu, Sanjib Kumar Panda, Andreas Lindemann, Vladimir Kaic, Mario Pacas, Jonathan W. Kimball, João Onofre Pereira Pinto, Philip T. Krein, Zhengming Zhao, Peter Wilson, Alireza Publicity, Roberto De Marca, Howard Michel, Marko Delimar, John W. Barr, Peter Staecker, Ralph Ford, Karen Bartleson, Jacek M. Żurada, Gary Blank, J.L. Hudgins, Dr Prendergast, Thomas Siegert, Business Administration, Matthew Loeb, Douglas Gorham, Eileen Lach, Corporate Compliance, Shannon Johnston, Ieee-Usa Chris Brantley, Alexander Pasik, Information Technology, Patrick Mahoney, Cecelia Jankowski, Fran Zappulla, Peter Tuohy, William Colacchio, Kristin Falco

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsPower electronicsElectronicsElectrical engineeringEngineeringComputer scienceVoltage

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.397
Threshold uncertainty score0.860

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3970.272

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 head0.011
GPT teacher head0.189
Teacher spread0.178 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2014
Admission routes1
Has abstractno

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