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Record W2162911696 · doi:10.1098/rsta.2007.2188

The application of the research work of James Clerk Maxwell in electromagnetics to industrial frequency problems

2008· article· en· W2162911696 on OpenAlexaff
David A. Lowther, E.M. Freeman

Bibliographic record

VenuePhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2008
Typearticle
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsAmpereMaxwell stress tensorFaraday cageMagnetic reluctanceMaxwell's equationsDisplacement currentElectromagneticsTorqueMagnetic fieldElectromagnetismMagnetic circuitComputer scienceMaxwell relationsElectromagnetic tensorElectromagnetic fieldElectrical engineeringElectromagnetic field solverCauchy stress tensorMechanical engineeringPhysicsClassical mechanicsVoltageMagnetEngineeringEngineering physicsOptical field

Abstract

fetched live from OpenAlex

Faraday's work inspired the development of electrical motors and generators. Until Maxwell pointed out the significance of Ampere's Law, there was no rigorous design method for magnetic devices. His interpretation strongly influenced the creation, by others, of the 'magnetic circuit' approach, which became the seminal design technique. This, utilizing the concept of reluctance, led to the design method for magnetic machines that is still widely in use today. The direct solution of the Maxwell equations (less the displacement current term) had to await the development of modern continuum methods to yield the field everywhere in, and around, the devices of interest, and this then permitted the application of the Maxwell stress tensor. This final refinement yielded forces and torques, and this resulted in the accurate prediction of electrical machine performance.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.009
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.002

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 head0.114
GPT teacher head0.325
Teacher spread0.211 · 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
GenreEmpirical

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

Citations4
Published2008
Admission routes1
Has abstractyes

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