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Record W2021776276 · doi:10.1002/jctb.2308

Fermentation of glucose and starch particles using an inexpensive medium

2009· article· en· W2021776276 on OpenAlexafffund
Nancy Bawa, Devin Bear, Gordon Hill, Catherine Hui Niu, William J. Roesler

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsStarchFermentationRaw materialFood scienceAmylaseEthanol fuelSugarHydrolysisYeastEthanolIngredientEthanol fermentationChemistryPulp and paper industryYield (engineering)BiotechnologyBiochemistryMaterials scienceOrganic chemistryBiologyEngineeringEnzyme

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: In North America, compared with sugar, starch is a more economical raw material to produce ethanol. Recently low temperature processing of starch granules directly to ethanol has been commercialized. With a view to minimizing process costs while maintaining a satisfactory ethanol yield, an inexpensive medium that does not require the addition of commercial α‐amylase has been developed for low temperature, direct fermentation of starch particles. RESULTS: Compared with an expensive medium that contained both yeast extract and α‐amylase, the inexpensive medium resulted in identical and 10% higher ethanol yields using glucose and starch granules as the raw substrates, respectively, but required longer processing times. Based on the different ingredient prices for expensive and inexpensive media, the operating cost to produce 10 million litres per year of ethanol utilizing simultaneous raw starch hydrolysis and fermentation is reduced over $ 2 million (Cdn) per year when using inexpensive medium. CONCLUSION: The new inexpensive medium and simultaneous raw starch hydrolysis and fermentation is considered the best strategy to produce ethanol directly from starch particles in North America. Copyright © 2009 Society of Chemical Industry

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 score0.014
Threshold uncertainty score0.415

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 integrity0.0010.000
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 head0.013
GPT teacher head0.282
Teacher spread0.268 · 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

Citations6
Published2009
Admission routes2
Has abstractyes

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