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Record W3203588066 · doi:10.1080/16583655.2021.1981802

Impact of pretreatment severity on fungal cellulase production on sugarcane bagasse substrate

2021· article· en· W3203588066 on OpenAlex
Muinat Olanike Kazeem, Lateefah Uthman-Saheed, Mushafau Adebayo Oke

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

VenueJournal of Taibah University for Science · 2021
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCellulaseBagasseCelluloseSubstrate (aquarium)ChemistryLimitingFood scienceEnzymePulp and paper industryBiotechnologyBiochemistryBiologyEngineeringEcology

Abstract

fetched live from OpenAlex

On-site production of cellulases using lignocellulosic materials can improve the economic viability of biorefineries. This, however, requires the pretreatment of substrates using thermochemical conditions that can vary in severity. To understand the effect of pretreatment severity on cellulase production by Aspergillus ustus S3 on sugarcane bagasse, we applied NaOH pretreatment corresponding to 3 severity factors (SF1.32, SF1.79, and SF3.64) to generate SCB that was used as inducing substrate. The highest cellulase activity (0.681 U/mL) was obtained with the intermediate severity (SF1.79) while significantly lower activities of 0.495 and 0.539 U/mL were recorded with low (SF1.32) and high (SF3.64) severities, respectively. Chemical and structural characterization revealed that low and intermediate severities improved cellulose accessibility and cellulase titres while high severity impaired them, thus limiting substrate suitability for enzyme production. These results show that though high SFs might be desirable in other applications, moderate severities may be more appropriate for cellulase 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.

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 score0.062
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.228
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