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Record W2944246511 · doi:10.1002/047134608x.w1431.pub2

Electrical Impedance Tomography

2019· other· en· W2944246511 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

VenueWiley Encyclopedia of Electrical and Electronics Engineering · 2019
Typeother
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrical impedance tomographyElectrical resistivity tomographyTomographyIterative reconstructionImage resolutionInverse problemBody surfaceBiomedical engineeringComputer scienceElectrical resistivity and conductivityPhysicsOpticsMedicineComputer visionEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Electrical impedance tomography (EIT) is a medical imaging technique that uses electrical stimulations and measurements at body‐surface electrodes. From these data, images of the distribution of conductivity within the body are calculated by solving an inverse problem. EIT has the advantage of producing high temporal resolution data, while being relatively low cost, noninvasive, small, and not using ionizing radiation. On the other hand, EIT has disadvantages in providing low spatial resolution and being sensitive to changes at the electrodes. EIT is currently being used clinically for monitoring of ventilated patients and is also being actively researched for applications such as cardiovascular flows and pressures, brain and nervous activity, cancer screening, and monitoring of gastrointestinal flows. EIT is similar to the electrical resistance tomography used in geophysical and process monitoring. This article reviews EIT from the point of view of its applications as well as image generation and interpretation.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.002
GPT teacher head0.171
Teacher spread0.169 · 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