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Record W2907969683 · doi:10.4236/aces.2019.91003

Hydrodynamic Modelling, Thermodynamic and Textural Variations during Common Beans Soaking

2019· article· en· W2907969683 on OpenAlex
Ebenezer Miezah Kwofie, Michael Ngadi

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

VenueAdvances in Chemical Engineering and Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsMcGill University
FundersInternational Fund for Agricultural Development
KeywordsSugarAbsorption of waterChemistryFood scienceThermodynamicsMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Hydrodynamic characteristics and its associated thermodynamic and textural variation of three common Malawian beans varieties (Boma, Sugar and Mandondo) during soaking were evaluated at four temperature regimes (25°C, 35°C, 45°C and 55°C). The equilibrium water uptake of 127% ± 5% was reached in 10, 6, and 4 hours respectively, for 25°C, 35°C and 45°C. Not much variation was observed between 45°C and 55°C except for sugar beans where equilibrium water uptake was reached within two hours of soaking at 55°C. Three models namely Peleg, two-parameter Mitscherlich model and viscoelastic model were used to evaluate the comparative predicting capabilities of the bean hydrodynamic characteristics. All models predicted the water absorption accurately (R2 > 0.903, RMSE activation kinetic parameters to be between 25 - 65 kJ/mol. Sugar beans were found to be the least hard. At room temperature, its hardness reduced by 58% within 2 hours of soaking. At higher temperature (55°C) hardness values were reduced to 12.5%, 11.1% and 15.0% within the first hour for Boma, Sugar and Mandondo beans, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.173

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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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