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Record W4242405874 · doi:10.1109/tpel.2020.3011887

IEEE Power Electronics Society Information

2020· article· en· W4242405874 on OpenAlexaff
Michael Kelly, Becky Boresen, Megan Cichocki, Alicia Tomaszewski, Dawn Melley, Kevin Lisankie, Peter Tuohy, Jeffrey Cichocki, Neelam Khinvasara, Katie Sullivan, Andreanna Mclean, Stephen Welby, Thomas Siegert, Julie Cozin, Donna Hourican, Jamie Moesch, Sophia Muirhead, Liesel Bell, Chris Brantley, Cherif Amirat, Karen Hawkins, Cecelia Jankowski, Konstantinos Karachalios, Standards Association, Mary Ward-Callan, Toshio Fukuda, Susan Kathy, Land, Kathleen Kramer, Joseph Lillie, José M. F. Moura, Stephen Phillips, Tapan Sarkar, Kukjin Chun, Robert Fish, Kazuhiro Kosuge, James Conrad, David B. Durocher, Jin Wang, Patrick Wheeler

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsPower electronicsElectronicsElectrical engineeringPower (physics)EngineeringComputer sciencePhysicsVoltage

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: Other · Consensus signal: Other
Teacher disagreement score0.411
Threshold uncertainty score0.840

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)0.4110.298

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 head0.019
GPT teacher head0.194
Teacher spread0.175 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractno

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