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Record W4283757132 · doi:10.1049/sbew555e_ch8

In vitro test-bed design for bone fracture monitoring using implanted antennas

2022· book-chapter· en· W4283757132 on OpenAlexaboutno aff
Symeon Symeonidis, William G. Whittow, Chinthana Panagamuwa

Bibliographic record

VenueInstitution of Engineering and Technology eBooks · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsnot available
Fundersnot available
KeywordsImaging phantomMaterials scienceMagnetic monopoleCortical boneBiomedical engineeringSection (typography)CylinderPhysicsEngineeringMechanical engineeringOpticsComputer scienceAnatomyMedicine

Abstract

fetched live from OpenAlex

In this chapter, the development and testing of two geometrical human body phantoms based on the recipes that were investigated in Chapter 4 is presented. The first is a two-material phantom consisting of bone cortical and muscle layers simulated in Section 8.1 and measured in Section 8.2. The second is a three-material geometrical phantom consisting of bone marrow, bone cortical and muscle layers simulated and measured in Sections 8.3 and 8.4 accordingly. The specific absorption rate (SAR) evaluation of the test-bed according to the USA, Canada, and European Union standards is conducted in Section 8.5. Two radiofrequency (RF) monopoles were implanted in a multi material phantom, and the condition of a bone fracture representative was replicated. The bone fracture was modelled as a cylinder residing in the mid distance between the implanted monopoles for the simulations. The technique was tested in the measurement section using three multi-material geometric phantoms. The results between simulation and measurements showed good agreement. In this section, the two-monopole system that is used for the simulations and measurements of this chapter is presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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.

Study designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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