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Record W6990005836

Cloning and characterization of new cellulases from Cellulomonas fimi and Cellulomonas flavigena

2013· dissertation· en· W6990005836 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2013
Typedissertation
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
FundersUniversity of WaterlooUniversity of Toronto
KeywordsCellulaseGlycoside hydrolaseCelluloseEnzymeCloning (programming)HydrolysisBeta-glucosidaseHydrolase
DOInot available

Abstract

fetched live from OpenAlex

Lignocellulose is one of the most abundant carbon sources in nature. This naturally-occuring substance is an underutilized source of bioenergy. A major bottleneck in biofuel processing is the enzymatic hydrolysis of lignocellulose into its ultimate fermentable product, glucose. Cellulomonas fimi is a well-studied soil organism known for its capabilities to efficiently hydrolyze cellulose. Recently sequenced genomes of Cellulomonas fimi and Cellulomonas flavigena have allowed analysis to reveal previously unidentified cellulases from several glycoside hydrolase (GH) families. This study also includes the expression of secreted cellulases from families GH 5, 6, and 9 at the protein level by the native organism after growth in media supplemented with carboxymethylcellulose or soluble xylan. In order to find enzymes with novel qualities, the cloning and expression of these newly identified cellulases from C. fimi and C. flavigena were done. One of these enzymes is Celf_1230 (Cel6C), a putative cellobiohydrolase from the glycoside hydrolase family 6. Using substituted cellulose derivatives as substrates, we have characterized Celf_1230 to be a thermostable enzyme with endoglucanase activity.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.163
Teacher spread0.156 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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