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Record W2752949206 · doi:10.1101/pdb.prot089102

Spectrophotometric Analysis of Ethanol and Glucose Concentrations in Yeast Culture Media

2017· article· en· W2752949206 on OpenAlexaff
Amy A. Caudy

Bibliographic record

VenueCold Spring Harbor Protocols · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsYeastEthanolChemistryFood scienceBiochemistryChromatography

Abstract

fetched live from OpenAlex

Fermentative growth on glucose is one of the most widely studied conditions of yeast growth in the laboratory. The production of ethanol from sugars is relevant to the wine, beer, and bread industries and to production of biofuels. Assaying the levels of glucose and ethanol in yeast growth medium allows the experimenter to determine the consumption of the carbon source glucose and the production of ethanol. This protocol describes enzyme-coupled assays for determination of glucose and ethanol concentrations in a sample of cell-free culture medium. Enzymes convert glucose or ethanol into other compounds through chemical reactions that reduce NAD(P) + to NAD(P)H, and the production of NAD(P)H is measured using a spectrophotometer. The methods presented are highly sensitive, with a detection limit of ∼0.4 mg/L of glucose and 50 mg/L of ethanol, and also have the advantage of high specificity. For example, glucose and fructose have identical chemical formulas and thus cannot be distinguished by a mass spectrometer, but the enzyme assay presented here is specific for glucose. The glucose assay can be coupled to other assays to determine the quantity of additional carbohydrates such as fructose, trehalose, and glycogen.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.274
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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