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Record W4399309637 · doi:10.3934/ammc.2024006

A boundary integral equations approach to electrical impedance tomography: Experiments on the KTC2023 data

2024· article· en· W4399309637 on OpenAlexaff
Spyros Alexakis, Adam R. Stinchcombe, Teemu Tyni

Bibliographic record

VenueApplied Mathematics for Modern Challenges · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrical impedance tomographySolverTomographyElectrical resistivity tomographyElectrical impedanceInverse problemBoundary (topology)Set (abstract data type)Computer scienceInverseApplied mathematicsMathematical analysisMathematicsMathematical optimizationGeometryMedicineElectrical resistivity and conductivityEngineeringElectrical engineeringRadiology

Abstract

fetched live from OpenAlex

In the fall of 2023 the Finnish Inverse Problems Society organized the Kuopio Tomography Challenge 2023 (KTC2023, see https://www.fips.fi/KTC2023.php). The aim of KTC2023 was to gather groups of contestants and test their various reconstruction methods on real electrical impedance tomography data. The main purpose of this paper is to demonstrate a boundary integral equation method (BIEM) based forward solver on the KTC2023 challenge data set. We also briefly summarize our BIEM formulation of the complete electrode model of electrical impedance tomography, as presented in the authors' previous work, and discuss its numerical implementation.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.866

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 head0.107
GPT teacher head0.296
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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