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Record W2804277228 · doi:10.23919/ropaces.2018.8364169

Biomedical magnetic induction tomography: An inhomogeneous green's function approach

2018· article· en· W2804277228 on OpenAlexaff
Philippe De Tillieux, Yves Goussard

Bibliographic record

Venue2018 International Applied Computational Electromagnetics Society Symposium (ACES) · 2018
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMagnetic fieldTomographyFinite element methodElectromagnetic inductionSalientIterative reconstructionCurrent (fluid)Green's functionComputer scienceFunction (biology)PhysicsOpticsComputer visionArtificial intelligenceElectromagnetic coil

Abstract

fetched live from OpenAlex

Magnetic induction tomography aims to reconstruct the passive electric properties of an object by measuring its scattered magnetic field. Current state-of-the-art numerical techniques are based on differential formulations such as the finite element method. A formulation based on volume integral equations has not yet been applied to its biomedical field and could improve the reconstruction speed by reducing the number of unknowns. This paper investigates salient characteristics of the approach and offers a solution based on inhomogeneous Green's functions.

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 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 score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.195
Teacher spread0.189 · 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.

Study designSimulation or modeling
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

Citations0
Published2018
Admission routes1
Has abstractyes

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