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Record W2107641205 · doi:10.1109/iembs.1997.756824

Human organ and tissue induced currents by 60 Hz electric and magnetic fields

2002· article· en· W2107641205 on OpenAlexafffund
M.A. Stuchly, Trevor W. Dawson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric fieldComputational physicsPhysicsMagnetic fieldElectric potentialCurrent densityElectric currentFinite-difference time-domain methodScalar (mathematics)Electromagnetic inductionDiscretizationMagnetic potentialScalar potentialComputationNuclear magnetic resonanceVoltageOpticsMathematical analysisComputer scienceClassical mechanicsMathematicsAlgorithmGeometryElectromagnetic coil

Abstract

fetched live from OpenAlex

The objective of the research presented was to reliably compute induced electric fields and currents in a realistic human model with a high resolution for exposures to 60 Hz electric and magnetic fields. All computations were for an anatomically-derived human full-body model discretized into a set of 3.6 mm cubes. For electric field induction a hybrid method was used. In this method a new quasi-static FDTD formulation was used to compute the fields with the lower resolution of 7.2 mm. The electric field at the body surface was then used to compute the surface charge density. The charge densities from the FDTD were interpolated onto a 3.6 mm grid and used as the source of the body interior potentials and electric fields in the scalar potential finite difference method (SPFD). For magnetic induction the SPFD method was used with the magnetic vector potential as the source. Organ average, organ maximum and spatial maps of the induced electric and current density fields were obtained. These dosimetric data for 30 different organs and tissues can be analyzed from various perspectives.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations6
Published2002
Admission routes2
Has abstractyes

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