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Investigation of morphophysiological properties of yeast promising for ethanol production

2024· article· en· W4405691348 on OpenAlexaff
Светлана Давыденко, Tatiana Meledina, Anastasia Andreeva

Bibliographic record

VenueProceedings of the Voronezh State University of Engineering Technologies · 2024
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsMiller Group (Canada)
Fundersnot available
KeywordsYeastEthanolEthanol fuelProduction (economics)ChemistryPulp and paper industryFood scienceBotanyEnvironmental scienceBiologyBiochemistryEngineeringEconomics

Abstract

fetched live from OpenAlex

Ethyl alcohol is a sought-after raw material in various sectors of the national economy, in particular in the food industry, medicine, and cosmetology. Recently, due to the need to reduce the burden on the environment, ethanol is used as a fuel, partially replacing gasoline and diesel fuel in internal combustion engines. In addition, for many countries that have a carbon deficit, replacing part of gasoline with ethanol is a solution to the problem of reducing fuel costs. Currently, 95% of ethanol produced from vegetable raw materials replaces 32% of gasoline. As a result of the work, it was found that to assess the effect of ethanol on the physiological activity of yeast, various control methods should be used. Thus, the absence of dead cells in the culture, as shown by the data obtained, cannot fully explain the metabolic activity of yeast in a medium with ethanol. Apparently, the intensity of ethanol stress is associated with complex, genetically determined processes, for example, activation of an extensive protein response and changes in the activity of ER enzymes. Comparison of the reproduction intensity of two strains of alcoholic yeast indicates the need to study their flocculation activity. In addition, when inoculating the medium, one must take into account the differences in cell size between different cultures, which can vary significantly. For example, yeast strain C16 is 33% larger than cells of strain C48. When comparatively assessing strains, only the quantitative determination of the concentration of cells in the inoculum should be used, and not the mass fraction of biomass in it.

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 score0.008
Threshold uncertainty score0.319

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 head0.016
GPT teacher head0.162
Teacher spread0.146 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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