MétaCan
Menu
Back to cohort
Record W4389684080 · doi:10.1007/s10570-023-05637-3

Comparative assessment of chemical and biochemical approaches for the activation of lignocellulosic materials and emerging opportunities for expansin-related proteins

2023· article· en· W4389684080 on OpenAlexaff
Salla Hiltunen, Janak Sapkota, Eleni Ioannou, Majid Haddad Momeni, Emma R. Master, Matti Ristolainen

Bibliographic record

VenueCellulose · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExpansinCellulosic ethanolCelluloseBiomass (ecology)CellulaseLignocellulosic biomassChemistryBiochemical engineeringBiochemistryBiologyEngineeringAgronomy

Abstract

fetched live from OpenAlex

Abstract Cellulose activation is a necessary step in many industrial processes including production of cellulose derivatives, regenerated cellulose, biofuels and biochemicals. Expansins and expansin-related proteins have been shown to disrupt the fibrillar aggregation and loosen the structure of lignocellulosic materials but typically lack lytic activity. Therefore, they offer a new but rather unexploited possibility for biomass to obtain better accessibility and reactivity. From an applied perspective, expansin-related proteins have been investigated for their potential to promote enzymatic hydrolysis of cellulosic substrates for the purpose of producing biofuels. The aim of this review is to compare conventional and emerging technologies relevant to cellulose activation, and critically evaluate the potential of expansin-related proteins for this purpose. As part of this assessment, methods to evaluate the action of expansin-related proteins on cellulosic substrates are summarized, and reported impacts are discussed in relation to source of the cellulosic substrate and treatment conditions. An outlook on prospective applications of expansin-related proteins is presented.

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 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)0.001
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 score0.005
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.158
GPT teacher head0.351
Teacher spread0.194 · 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 teacher head, 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCelluloseSame topicAdvanced Cellulose Research StudiesFrench-language works237,207