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Record W2571713452 · doi:10.1109/cama.2016.7815790

A volumetric integral equation formulation for magnetic induction tomography

2016· article· en· W2571713452 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

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicNumerical methods in inverse problems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsEddy currentIntegral equationMagnetic fieldElectromagnetic inductionInverse problemMathematical analysisVolume integralMagnetostaticsMathematicsPhysicsElectromagnetic coil

Abstract

fetched live from OpenAlex

In magnetic induction tomography of biological tissue, an alternating magnetic field is used to induce eddy currents inside tissues, which in turn generate a secondary magnetic field. This perturbation of the field depends on the dielectric properties of the tissue and, through resolution of the inverse problem, a map of these properties can be reconstructed. Current state-of-art methods either rely on the quasi-static and low conductivity approximations or are based on differential solvers or surface integral equations. A formulation based on volumetric integral equations offers clear advantages such as a better accuracy than the approximative methods, a smaller solution domain than differential solvers and a better conditionning than surface integral methods. The aim of this paper is to formulate the eddy current problem in terms of a volume integral equation and to show its validity.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.646
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.154
GPT teacher head0.364
Teacher spread0.210 · 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

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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