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Record W2031028063 · doi:10.1186/1753-6561-5-s7-p176

Proteomic analysis of Clostridium acetobutylicumin butanol production from lignocellulosic biomass

2011· article· en· W2031028063 on OpenAlexaff
Kumaran Sivagnanam, Vijaya G.S. Raghavan, Manesh Shah, Nathan C. VerBerkmoes, Robert L. Hettich, Mark Lefsrud

Bibliographic record

VenueBMC Proceedings · 2011
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsMcGill University
Fundersnot available
KeywordsClostridium acetobutylicumButanolLignocellulosic biomassBiofuelBiomass (ecology)FermentationBiotechnologyBioenergyPulp and paper industryChemistryFood scienceBiochemical engineeringBiochemistryBiologyAgronomyEthanolEngineering

Abstract

fetched live from OpenAlex

Plant biomass is an abundant and renewable source of energy rich carbohydrates that can be efficiently converted by microbes into biofuels. Butanol is considered as a second generation biofuel when it is produced from lignocellulosic biomass comprising of agricultural and garden wastes that does not compete with the food supplies. Clostridium acetobutylicum is a gram positive, spore forming, obligately anaerobic bacteria capable of converting different sugars from lignocellulosic biomass to butanol through acetone – butanol – ethanol (ABE) fermentation process. However, the production of butanol from ABE fermentation process is not economically viable and studies have been performed to understand the utilization of lignocellulosic biomass and regulation of butanol production to improve butanol productivity. Successful industrial butanol production process through ABE fermentation requires complete understanding of the C .acetobutylicum. Shotgun proteomics provides a direct approach to study the whole proteome of an organism at molecular level in depth. Therefore, this paper focuses on shotgun proteomic profiling of C. acetobutylicum ATCC 824 from butanol fermentation process, elucidating the molecular functional mechanisms of C.acetobutylicum in butanol production.

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 score0.011
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.199
Teacher spread0.174 · 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
Published2011
Admission routes1
Has abstractyes

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