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Biomedical Imaging and Impressioning using Low-Frequency Electromagnetic Energy

2024· article· en· W4403211132 on OpenAlexaff
Omar M. Ramahi, Hamid A. Chelaresi, Mauricio Fernandez, Ghazaleh Tashtarian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceEnergy (signal processing)Physics

Abstract

fetched live from OpenAlex

The advent of X-Ray Computerized Tomography using Radon transforms for imaging of human bodies in the 1970s gave rise to a strong interest in imaging using microwaves. The primary thrust behind such interest lied in the non-harmful effects of low-power microwaves in comparison to potentially ionizing X-rays. The field of imaging using microwaves has traditionally evolved based on inverse scattering theories that were steeped in integral equations’ formulations of the electromagnetic radiation or acoustic problems. Inverse scattering problems were fundamentally based on reconstructing the dielectric permittivity or magnetic permittivity profile of a specific medium or object. In biomedical imaging, however, reconstructing the precise concise profile of the object (such as the human body), is not of importance but rather generating an image or an impression that indicates the presence of an anomaly such as a fracture in a bone, a blood clot, or a tumorous growth within the body. In these modalities, the primary goal is to create a contrast between the constituents of the human body. Therefore, mathematically complex ill-posed inverse scattering methods may not be most suitable or even practical for imaging or impressioning human bodies.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.232
Teacher spread0.228 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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