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Record W2107687653 · doi:10.1002/cjce.20089

Modelling spatial distributions of moisture and quality during drying of granular baker's yeast

2008· article· en· W2107687653 on OpenAlexvenueno aff
Uğur Yüzgeç, Mustafa Fehmi Türker, Yaşar Becerikli

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsGranule (geology)ShrinkageMoistureGranular materialMaterials scienceParticle sizeMechanicsWater contentThermodynamicsComposite materialChemistryPhysicsGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract A distributed parameter model was developed to predict the drying behaviour of granular baker's yeast by setting up material and heat balances at the particle level. Temperature and moisture gradients were calculated for cylindrical and spherical granules. The performance of the model with two granule sizes was compared with experimental measurements. The model was initially used for non‐shrinking granules but later modified to take shrinkage into account. The reduction in granule size during the course of drying was estimated and good correspondence with experimental measurements was obtained. In addition to temperature and moisture gradients, the product quality was predicted during drying and compared to experimental results. The accuracy of the model was better than the lumped parameter model.

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 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 score0.032
Threshold uncertainty score0.993

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 head0.024
GPT teacher head0.186
Teacher spread0.162 · 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.

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

Citations15
Published2008
Admission routes1
Has abstractyes

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