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Record W1523106711 · doi:10.1002/jnm.831

Finite element modeling for optimization of microwave heating of in‐shell eggs and experimental validation

2011· article· en· W1523106711 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
FundersOffice of International Science and Engineering
KeywordsMicrowave heatingShell (structure)MicrowaveFinite element methodProcess (computing)Nuclear engineeringPasteurizationDesign of experimentsMaterials scienceMechanical engineeringProcess engineeringEngineeringComputer scienceStructural engineeringChemistryMathematicsTelecommunications

Abstract

fetched live from OpenAlex

SUMMARY Considering microwave (MW) heating as a viable alternative for in‐shell pasteurization of eggs, after the simulation of the MW heating process by using a finite element model, process optimization was carried out to determine the most effective procedure and design for the process. The varying parameters obtained by using different modeling techniques for MW heating of in‐shell eggs were optimized. Laboratory‐scale experimental trials were conducted to test the validity and effectiveness of the optimized parameters. The optimal parameters set forth were found to be more efficient in terms of heating time and uniformity. MW heating appeared to be a viable alternative for the pasteurization of in‐shell eggs. Copyright © 2011 John Wiley & Sons, Ltd.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.261

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 head0.029
GPT teacher head0.306
Teacher spread0.277 · 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