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Record W1976330075 · doi:10.1080/07373937.2011.584999

Microwave-Assisted Thin Layer Drying of Wheat

2011· article· en· W1976330075 on OpenAlexafffundabout
Mohamed Hemis, C. B. Singh, Digvir S. Jayas

Bibliographic record

VenueDrying Technology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThin layerLayer (electronics)Materials scienceMicrowaveComposite materialEnvironmental scienceChemical engineeringPulp and paper industryComputer scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Drying characteristics of Canada Western Red Spring (CWRS) wheat were studied using a domestic microwave convective oven. The effects of microwave power level, grain bed thickness, and initial grain moisture on the drying kinetics were investigated. Wheat samples with initial moisture levels of 0.18 to 0.29 kg water/kg of dry matter were dried for different drying periods of 180 to 360 s. The moisture loss data were recorded at regular short intervals. Then moisture loss data were fitted to various models (Page equation, modified drying equation, and Midilli equation) to study the drying kinetics of wheat. The results showed that wheat moisture loss increased with increasing microwave power level. A mathematical model was developed by coupling mass and energy balances, resulting in a system of non-linear equations. The predicted moisture loss data from the developed model were compared by fitting to experimental microwave data that were in good agreement.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 head0.072
GPT teacher head0.231
Teacher spread0.159 · 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 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

Citations35
Published2011
Admission routes3
Has abstractyes

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