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Record W4320495582 · doi:10.1002/bbb.2481

Progress and development of syngas fermentation processes toward commercial bioethanol production

2023· article· en· W4320495582 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiofuels Bioproducts and Biorefining · 2023
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSyngasBiofuelBiomass (ecology)FermentationChemistryPulp and paper industryGas compositionChemical engineeringWaste managementFood scienceCatalysisOrganic chemistryEngineeringBiologyEcologyThermodynamics

Abstract

fetched live from OpenAlex

Abstract Syngas is created through the thermochemical conversion of biomass using gasification or pyrolysis and from CO‐rich off‐gases obtained from industries such as steel mills. The Wood–Ljungdahl metabolic pathway, or one of its variations, is used by acetogenic bacteria to convert syngas components (CO, H 2 , and CO 2 ) to alcohols and other compounds. Many factors affect how well syngas is fermented, including the bacteria species used, syngas composition, medium components, bioreactor type, operational parameters used and the gas–liquid mass transfer rate. These parameters impact carbon and electron flow in the bacteria, influencing the distribution, concentration and metabolic end‐product yield, which determines process feasibility. This article focuses on gas composition, microorganisms, gas–liquid mass transfer fermentation strategies, medium design and commercialization activities to develop the syngas fermentation processes. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

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.

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 score0.470
Threshold uncertainty score0.710

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 head0.045
GPT teacher head0.258
Teacher spread0.214 · 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