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Record W4239884404 · doi:10.22215/etd/2016-11611

Geophysical Applications of Electrical Impedance Tomography

2016· dissertation· en· W4239884404 on OpenAlexaff
Alistair Boyle

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrical impedance tomographyElectrical resistivity tomographyComputer scienceTomographyGeophysicsCorrectnessElectrical impedanceGeologyLandslideRemote sensingElectrical resistivity and conductivityEngineeringGeotechnical engineeringElectrical engineeringAlgorithmPhysics

Abstract

fetched live from OpenAlex

Impedance imaging is a technique where stimulus currents are applied through electrodes to a body or the ground and measurements of the potential at other electrodes are collected.The data, along with any available prior information, are used to reconstruct an image of the conductivity distribution throughout the interior which provides diagnostic, cost effective information upon which decisions can be based for a broad array of geophysics, biomedical and industrial applications.The same technique is known as (biomedical) Electrical Impedance Tomography (EIT) and (geophysics) Electrical Resistivity Tomography (ERT).New geophysical applications have arisen for the automated monitoring of slope stability risks for natural landslides, transport embankments and cuttings, mine tailings dams and piles, and remote infrastructure in changing climatic environments.When impedance imaging Thank you to the many people who made this work such a rewarding experience:

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.205
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2016
Admission routes1
Has abstractyes

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