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Record W2908590543 · doi:10.4236/aces.2019.91005

Hydration Characteristics of Selected Varieties of Paddy Rice from Nigeria

2019· article· en· W2908590543 on OpenAlexaff
Chijioke Kingsley Ejebe, Ebenezer Miezah Kwofie, Michael Ngadi

Bibliographic record

VenueAdvances in Chemical Engineering and Science · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsAbsorption of waterArrhenius equationChemistryWater contentThermal diffusivityHorticultureFood scienceAnimal scienceMaterials scienceActivation energyBiologyComposite materialPhysicsThermodynamicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, water absorption characteristics of some rice varieties (Bisalayi, FARO 61, FARO 60, FARO 52 and FARO 44) from Nigeria were studied at 30°C, 45°C, 60°C and 75°C by determining the increase in grain weight as a function of time during soaking. Differences in moisture content among the selected varieties of paddy during soaking were significant (P 2 of (0.834 - 0.997). Water absorption rate was found to increase with soaking temperature, while water saturation time decreased with temperature. Average values of diffusion coefficients of moisture during soaking of paddy rice at different temperatures (30°C, 45°C, 60°C and 75°C) were estimated as 6.25 × 10 -11, 6.28 × 10 -11, 7.02 × 10 -11, and 5.51 × 10 -11 and 5.52 × 10 -11 m2/s for Bisalayi, FARO 61, FARO 60, FARO 52 and FARO 44, respectively. The activation energies of the diffusivity through different varieties of rice grains werecalculated using Arrhenius-type equation for diffusion dependence on temperature and were determined as 41.96, 38.69, 40.16, 34.05 and 42.12 kJ/mole for Bisalayi, FARO 61, FARO 60, FARO 52 and FARO 44 for the respectively rice variety above.

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 score0.019
Threshold uncertainty score0.186

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 head0.209
Teacher spread0.205 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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