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Record W2014864634 · doi:10.1109/mpel.2014.2312275

Electric Motors in Electrified Transportation: A step toward achieving a sustainable and highly efficient transportation system

2014· article· en· W2014864634 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Power Electronics Magazine · 2014
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAutomotive industryAutomotive engineeringTraction motorInternal combustion engineElectric motorTraction (geology)Automotive engineBattery electric vehicleEfficient energy useFossil fuelElectric vehicleEngineeringMechanical engineeringElectrical engineeringWaste management

Abstract

fetched live from OpenAlex

The transportation sector is one of the largest energy users, and the main source of energy in our transportation system is still fossil fuels. As an example, in the United States, 98% of transportation energy comes from oil, but most of it is wasted due to the low efficiency of con-ventional internal combustion engine (ICE) vehicles. To-day's low fuel efficiencies make the automotive industry one of largest sources of greenhouse gas emissions. In this article, the multidisciplinary nature of electric traction motors is investigated and related design issues are presented for interior permanent magnet (PM), induction, and switched reluctance machines (SRMs). These are the commonly considered machine types for traction applications, although the PM machine is the most widely used type in currently available electrified vehicles. The operating principles of these machines were also be explained.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.182
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