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Record W2327794776 · doi:10.1515/ijfe-2014-0039

Finite Element Model to Predict the Bioconversion Rate of Glucose to Fructose using <i>Escherichia coli</i> K12 for Sugar Production from Date

2015· article· en· W2327794776 on OpenAlex

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

VenueInternational Journal of Food Engineering · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsBioconversionFructoseFluentSugarVolume (thermodynamics)ChemistryComputational fluid dynamicsEscherichia coliEulerian pathChromatographyBiochemistryThermodynamicsMathematicsPhysicsFermentationApplied mathematics

Abstract

fetched live from OpenAlex

Abstract In this study, Bioconversion process of glucose to fructose from date syrup using Escherichia coli K12 is modeled using a commercial computational fluids dynamics (CFD) code fluent FLUENT 6.3.23 [8] which we implemented a user-defined functions (UDF) to simulate the interrelationships at play between various phases. A two phases CFD model was developed using an Eulerian – Eulerian approach to calculate the fructose volume fraction produced during time. The bioconversion process was studied as function of three initial concentration of glucose (0.14, 0.242 and 0.463gL –1 ), three induction time (60, 120 and 180 mn) and three inoculum volume (100, 120 and 150mL). The numerical results are compared with experimental data for bioconversion rate and show good agreement (R 2 = 0.894). The optimal condition of diffusion was obtained by applying an initial concentration of glucose less than 0.2gL –1 and induction time great than 100 minutes.

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)0.001
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 score0.381
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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