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Record W2960441079 · doi:10.5281/zenodo.2691705

Proceedings of the 20th International Conference on Biomedical Applications of Electrical Impedance Tomography

2019· article· en· W2960441079 on OpenAlexaff
Alistair Boyle, Kirill Aristovich, Anna Witkowska-Wrobel, David Holder

Bibliographic record

VenueARAN (University of Galway Research Repository) (Ollscoil na Gaillimhe – University of Galway) · 2019
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
FundersCongressionally Directed Medical Research ProgramsNIHR Imperial Biomedical Research CentreJapan Society for the Promotion of ScienceMedical Research CouncilDefense Advanced Research Projects AgencyBusiness FinlandChiba UniversityEuropean Social FundNational Research Foundation of KoreaThayer School of Engineering, Dartmouth CollegeState Scholarships FoundationNational Institute for Health and Care ResearchBundesministerium für Bildung und ForschungNational Research FoundationEesti TeadusagentuurNational Natural Science Foundation of ChinaIrish Research CouncilEuropean CommissionNational Institutes of HealthMinistry of Science, ICT and Future PlanningEngineering and Physical Sciences Research CouncilDartmouth College
KeywordsElectrical impedance tomographyTomographyElectrical resistivity tomographyEngineering physicsMedical physicsMedicineEngineeringElectrical engineeringRadiologyElectrical resistivity and conductivity

Abstract

fetched live from OpenAlex

Proceedings of the 20th International Conference on Biomedical Applications of Electrical Impedance Tomography Edited by Alistair Boyle, Kirill Aristovich, Anna Witkowska-Wrobel and David Holder This document is the collection of papers accepted for presentation at the 20th International Conference on Biomedical Applications of Electrical Impedance Tomography.<br> <br> Each individual paper in this collection: © 2019 by the indicated authors. Collected work: © 2019 Alistair Boyle, Kirill Aristovich, Anna Witkowska-Wrobel and David Holder

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.011
GPT teacher head0.213
Teacher spread0.201 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

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