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Record W4225156228 · doi:10.3390/polym14091806

Carbohydrate-Binding Modules of Potential Resources: Occurrence in Nature, Function, and Application in Fiber Recognition and Treatment

2022· review· en· W4225156228 on OpenAlexaff
Yena Liu, Peipei Wang, Jing Tian, Farzad Seidi, Jiaqi Guo, Wenyuan Zhu, Huining Xiao, Junlong Song

Bibliographic record

VenuePolymers · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsUniversity of New Brunswick
FundersNational Natural Science Foundation of China
KeywordsCarbohydrate-binding moduleFunction (biology)Domain (mathematical analysis)Substrate (aquarium)Molecular recognitionBiochemical engineeringFiberComputer scienceGlycoside hydrolaseMaterials scienceChemistryBiochemistryBiologyEngineeringMathematicsEnzymeCell biologyOrganic chemistryMoleculeComposite material

Abstract

fetched live from OpenAlex

Great interests have recently been aroused in the independent associative domain of glycoside hydrolases that utilize insoluble polysaccharides-carbohydrate-binding module (CBM), which responds to binding while the catalytic domain reacts with the substrate. In this mini-review, we first provide a brief introduction on CBM and its subtypes including the classifications, potential sources, structures, and functions. Afterward, the applications of CBMs in substrate recognition based on different types of CBMs have been reviewed. Additionally, the progress of CBMs in paper industry as a new type of environmentally friendly auxiliary agent for fiber treatment is summarized. At last, other applications of CBMs and the future outlook have prospected. Due to the specificity in substrate recognition and diversity in structures, CBM can be a prosperous and promising 'tool' for wood and fiber processing in the future.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations50
Published2022
Admission routes1
Has abstractyes

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