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Record W4223487735 · doi:10.1109/mias.2022.3148628

Simulation Versus Experimental Verification [Society News]

2022· article· en· W4223487735 on OpenAlexaff
Pete Wung, Sudipta Chakraborty, Joel Van Sickel, Ayman El‐Refaie, Takashi Yamada

Bibliographic record

VenueIEEE Industry Applications Magazine · 2022
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsOpal-Rt Technologies (Canada)University of Calgary
Fundersnot available
KeywordsComputer scienceReliability engineeringSystems engineeringEngineering

Abstract

fetched live from OpenAlex

During the recent 2021 IEEE Energy Conversion Congress and Exposition (ECCE 2021) conference, a panel session was organized to discuss a topic important to engineering in general, one that is focused specifically on the power electronics and electric machines milieu for the sake of narrowing the discussion: the changing roles of simulation and experimentation as the technology in both areas advances.

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)0.012
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0020.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0430.015

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 head0.026
GPT teacher head0.272
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations6
Published2022
Admission routes1
Has abstractyes

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