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Record W1966278558 · doi:10.1007/s10295-014-1533-1

Microbial diversity in sugarcane ethanol production in a Brazilian distillery using a culture-independent method

2014· article· en· W1966278558 on OpenAlexfundno aff
Ohana Yonara Assis Costa, Betulia M. Souto, Daiva Domenech Tupinambá, Jessica Carvalho Bergmann, Cynthia Maria Kyaw, Ricardo Henrique Krüger, Cristine Chaves Barreto, Betânia Ferraz Quirino

Bibliographic record

VenueJournal of Industrial Microbiology & Biotechnology · 2014
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCanadian Centre for Applied Research in Cancer Control
KeywordsBiologyPyrosequencingArchaeaBacteriaEthanol fuelMicrobial ecologyFermentationFood scienceInternal transcribed spacerBiotechnologyMicrobiologyBotanyRibosomal RNAGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

Sugarcane ethanol production occurs in non-sterile conditions, and microbial contamination can decrease productivity. In this study, we assessed the microbial diversity of contaminants of ethanol production in an industrial facility in Brazil. Samples obtained at different stages were analyzed by pyrosequencing-based profiling of bacterial and archaeal 16S rRNA genes and the fungal internal transcribed spacer region. A total of 355 bacterial groups, 22 archaeal groups, and 203 fungal groups were identified, and community changes were related to temperature changes at certain stages. After fermentation, Lactobacillus and unclassified Lactobacillaceae accounted for nearly 100 % of the bacterial sequences. Predominant Fungi groups were "unclassified Fungi," Meyerozyma, and Candida. The predominant Archaea group was unclassified Thaumarchaeota. This is the first work to assess the diversity of Bacteria, and Archaea and Fungi associated with the industrial process of sugarcane-ethanol production using culture-independent techniques.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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.

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

Citations63
Published2014
Admission routes1
Has abstractyes

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