MétaCan
Menu
Back to cohort
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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.009

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 teacher head, not a consensus.

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

Explore more

Same venueIEEE DataPortFrench-language works237,207