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Record W4237584477 · doi:10.1109/temc.2020.2967254

IEEE Electromagnetic Compatibility Society

2020· article· en· W4237584477 on OpenAlexaff
Jim Knighten, Farhad Rachidi, Perry F. Wilson, Bruce Archambeault, J. Lasalle, J O'neil, Frank Sabath, Alex Duffy, Heyno Garbe, Services Hare, Standards Bunting, Tom Braxton, Ross Carlton, C.K. Chan, Lawrence S. Cohen, J Hill, Hiroshi Inoue, Jun Fan, Frank Leferink, John D. Norgard, Claudia Sartori, Michael F. Violette, Xinxin Ye, R. F. Davis, Józef Drewniak, Kris Hatashita, Susanne Kaule, Pieter Gideon Wiid, Tzong‐Lin Wu, Dawn Melley, Kevin Lisankie, Peter Tuohy, Jeffrey Cichocki, Neelam Khinvasara, Flavio Canavero, Luk R. Arnaut, Kenjiro Fukuda, Susan Kathy, Land, Kathleen Kramer, Joseph Lillie, José M. F. Moura, Stephen Phillips, Kripasindhu Sarkar, Kukjin Chun, Robert Fish, Kazuhiro Kosuge, James Conrad, Stephen Welby, Thomas Siegert, Julie Cozin, Donna Hourican, Jamie Moesch, Jack Bailey, Cherif Amirat, Karen Hawkins, Cecelia Jankowski, Geographic Activities, Michael J. Forster, Konstantinos Karachalios, Standards Association, Mary Ward-Callan, John Verboncoeur, Jun Chen, Marcos Rubinstein, Marco Leone, Ding‐Bing Lin, Keyhan Sheshyekani, Zhi Yang, Akimasa Hirata, Erjia Liu, Alexandre Piantini, Sergio A. Pignari

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2020
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsPolytechnique MontréalCanadian Standards Association
Fundersnot available
KeywordsElectromagnetic compatibilityCompatibility (geochemistry)Electromagnetic interferenceComputer scienceElectronic engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

with principal professional interest in electromagnetic compatibility.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.148
Threshold uncertainty score0.496

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1480.151

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 head0.025
GPT teacher head0.221
Teacher spread0.196 · 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 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 abstractyes

Explore more

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