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Record W2923496841 · doi:10.1128/aem.03130-18

Composition and Origin of the Fermentation Microbiota of Mahewu, a Zimbabwean Fermented Cereal Beverage

2019· article· en· W2923496841 on OpenAlexafffund
Felicitas Pswarayi, Michael G. Gänzle

Bibliographic record

VenueApplied and Environmental Microbiology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates Bio SolutionsCanada Research ChairsGovernment of CanadaSchlumberger Foundation
KeywordsFermentationStarterBiologyEnterobacteriaceaeFood scienceMicrobiomeFermentation starterComposition (language)BacteriaGut floraAmplicon sequencing16S ribosomal RNAMicrobiologyBiotechnologyGeneticsBiochemistryEscherichia coliGeneLactic acid

Abstract

fetched live from OpenAlex

This study provides insight into the composition and origin of the microbiota of mahewu and the composition of millet malt microbiota. Fermentation microbiota are often hypothesized to be derived from the environment, but the evidence remains inconclusive. Our findings confirm that millet malt is the major source of mahewu microbiota. By complementing culture methods with high-throughput sequencing of 16S rRNA amplicons and strain-specific quantitative PCR, this study provides evidence about the source of mahewu microbiota, which can inform the development of starter cultures for mahewu production. The study also documents the fate of Enterobacteriaceae during the fermentation of mahewu. There are concerns regarding the safety of traditionally prepared mahewu, and this requires in-depth knowledge of the fermentation process. Therefore, this study elucidated millet malt microbiota and identified cultures that are able to control the high numbers of Enterobacteriaceae that are initially present in mahewu fermentations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
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 head0.157
Teacher spread0.153 · 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

Citations90
Published2019
Admission routes2
Has abstractyes

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