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Record W6888543811 · doi:10.21227/n9b1-6c76

Dielectric Measurements of Ground Beef in Microwave Frequencies at Different Hydration Levels

2023· dataset· en· W6888543811 on OpenAlexaff

Bibliographic record

VenueIEEE DataPort · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicrowaveDielectricPermittivityDehydrationConductivityBiological tissueCharacterization (materials science)Water contentDielectric loss

Abstract

fetched live from OpenAlex

Readily available animal tissue such as ground beef is a convenient material for mimicking the dielectric propertiesof biological tissue when validating microwave imaging and sensing hardware and techniques. The reliable use of these materialsdepends on the accurate characterization of their properties. Tissue water content is a dominant factor in microwave frequency tissueReadily available animal tissue such as ground beef is a convenient material for mimicking the dielectric properties of biological tissue when validating microwave imaging and sensing hardware and techniques. The reliable use of these materials depends on the accurate characterization of their properties. Tissue water content is a dominant factor in microwave frequency tissue properties, thus the effect of dehydration must be considered. The dependence of tissue properties on hydration is also important for new applications of microwave sensing for hydration monitoring. A new protocol for measuring the dielectric properties of heterogeneous tissue and rigorously analyzing the results is presented. The effect of dehydration on the permittivity and conductivity of ground beef samples is explored. A linear mixed effect model was employed to examine the impact of the frequency-dependent behaviour of the dielectric properties. As expected, dehydration impacts both the permittivity and conductivity of ground beef samples with a larger influence on permittivity.properties, thus the effect of dehydration must be considered. The dependence of tissue properties on hydration is also importaReadily available animal tissue such as ground beef is a convenient material for mimicking the dielectric properties of biological tissue when validating microwave imaging and sensing hardware and techniques. The reliable use of these materials depends on the accurate characterization of their properties. Tissue water content is a dominant factor in microwave frequency tissue properties, thus the effect of dehydration must be considered. The dependence of tissue properties on hydration is also important for new applications of microwave sensing for hydration monitoring. A new protocol for measuring the dielectric properties of heterogeneous tissue and rigorously analyzing the results is presented. The effect of dehydration on the permittivity and conductivity of ground beef samples is explored. A linear mixed effect model was employed to examine the impact of the frequency-dependent behaviour of the dielectric properties. As expected, dehydration impacts both the permittivity and conductivity of ground beef samples with a larger influence on permittivity.for new applications of microwave sensing for hydration monitoring. A new protocol for measuring the dielectric properties ofheterogeneous tissue and rigorously analyzing the results is presented. The effect of dehydration on the permittivity and conductivityof ground beef samples is explored. A linear mixed effect model was employed to examine the impact of the frequency-dependentbehaviour of the dielectric properties. As expected, dehydration impacts both the permittivity and conductivity of ground beefsamples with a larger influence on permittivity.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.128
GPT teacher head0.311
Teacher spread0.183 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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