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Record W4252577544 · doi:10.32920/ryerson.14661468

The Performance Of Clostridium Phytofermentans For Biofuels Production From Lignocellulosic Biomass

2021· preprint· en· W4252577544 on OpenAlexafffundabout
Benjamin Percy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsToronto Metropolitan University
FundersOntario Centres of Excellence
KeywordsBiofuelLignocellulosic biomassPulp and paper industryBiomass (ecology)Ethanol fuelXyloseRaw materialCelluloseCellobioseChemistryBioenergyFood scienceFermentationEnvironmental scienceWaste managementCellulaseAgronomyOrganic chemistryEngineeringBiology

Abstract

fetched live from OpenAlex

Ethanol produced from lignocellulosic biomass is an alternative transportation fuel with the potential to lower greenhouse gas emissions and increase energy security. Source-separated organic waste (SSO) from the city of Toronto was investigated for feasibility as a lignocellulosic ethanol feedstock. Clostridium phytofermentans is a mesophilic, cellulolytic and ethanologenic species with potential application for ethanol production from lignocullulosic biomass. C. phytofermentans was considered for biofuels production through experiments on a variety of substrates including soluble sugars and pure celluloses. Results from this study found that C. phytofermentans produced 73% of the theoretical ethanol yield on cellobiose but grew poorly on glucose and xylose. In addition, C. phytofermentans grew marginally on microcrystalline and ball-milled cellulose, but with supplemental enzymes produced 55% of the theoretical ethanol yield.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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 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

Citations4
Published2021
Admission routes3
Has abstractyes

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