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Record W2988229038 · doi:10.1021/acsomega.9b02993

Mild Acid-Catalyzed Atmospheric Glycerol Organosolv Pretreatment Effectively Improves Enzymatic Hydrolyzability of Lignocellulosic Biomass

2019· article· en· W2988229038 on OpenAlex
Kaneza Pascal, Hongyan Ren, Fubao Sun, Shuxian Guo, Jinguang Hu, Jing He

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

VenueACS Omega · 2019
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Calgary
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceState Administration of Foreign Experts AffairsGovernment of Jiangsu ProvinceMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsOrganosolvHemicelluloseLignocellulosic biomassCelluloseLigninChemistryBagasseCellulaseGlycerolCentral composite designBiomass (ecology)Substrate (aquarium)Response surface methodologyPulp and paper industryChromatographyBiochemistryOrganic chemistryAgronomyBiology

Abstract

fetched live from OpenAlex

addition, wherein the hemicellulose and lignin removal rates were 82 and 52%, respectively, with extremely high cellulose retention of 98%. The ac-AGO-pretreated substrate exhibited good enzymatic hydrolyzability at a modest cellulase loading, affording a 70% glucose yield after 72 h. Multiple analysis tools were used to correlate the hydrolyzability of the substrate with its structural features. The results indicated that the mild ac-AGO pretreatment can modify the lignocellulosic biomass structure to achieve good hydrolyzability, mainly resulting in significant hemicellulose removal.

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 score0.004
Threshold uncertainty score0.871

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 head0.005
GPT teacher head0.189
Teacher spread0.183 · 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