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Record W1972305234 · doi:10.1002/ceat.201300253

Baker's Yeast Behavior during Vacuum Agitated Contact Drying

2013· article· en· W1972305234 on OpenAlexaff
Lyes Bennamoun, Muhammad T. Afzal, Angélique Léonard

Bibliographic record

VenueChemical Engineering & Technology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of New Brunswick
FundersFonds De La Recherche Scientifique - FNRS
KeywordsImpellerYeastMass transferFluidized bedWork (physics)Materials scienceFluidizationChemistryVacuum dryingPhase (matter)MechanicsChromatographyMechanical engineeringEngineeringFreeze-dryingPhysics

Abstract

fetched live from OpenAlex

Abstract Drying is considered as an intensive operation that consumes large quantities of energy. Usually, baker's yeast is obtained using freeze drying or fluidized‐bed drying, both of which are considered as expensive technologies. So, exploring other techniques such as contact drying could limit this disadvantage. In addition, no work dealing with contact drying of baker's yeast has been accomplished yet. Therefore, here, the behavior of baker's yeast during vacuum agitated contact drying is presented. The results show that the drying process can be divided into three phases: the pasty phase, the lumpy phase, and the granular phase. The influence of the drying parameters, such as the temperature, the impeller velocity, and the initial mass, was also studied. It was found that the wall temperature and the impeller velocity have a positive effect on the drying kinetics, as their increase allows a reduction in the drying time. Nevertheless, an increase in the pressure level or the initial mass of the product caused the drying time to increase.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.222
Teacher spread0.217 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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