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Record W2477466081 · doi:10.1021/bk-2001-0769.ch006

An Overview of Factors Influencing the Enzymatic Hydrolysis of Lignocellulosic Feedstocks

2000· book-chapter· en· W2477466081 on OpenAlex
Ali R. Esteghlalian, Vinit Srivastava, Neil R. Gilkes, David J. Gregg, John N. Saddler

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 symposium series · 2000
Typebook-chapter
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLignocellulosic biomassBiofuelBiomass (ecology)Renewable energyGasolineBiochemical engineeringEnzymatic hydrolysisEnvironmental scienceWaste managementRenewable fuelsPulp and paper industryHydrolysisChemistryEngineeringOrganic chemistryAgronomy

Abstract

fetched live from OpenAlex

Lignocellulosic biomass conversion to ethanol promises to provide an environmentally benign alternative fuel that can reduce the consumption of gasoline, thereby reducing our dependence on a non-renewable energy source and improving the urban air quality. The process of microbial degradation of biomass is not understood at the molecular level and yet it is clear that many microorganisms have evolved diverse mechanisms to accomplish this task. We review current concepts regarding enzymatic hydrolysis of bioethanol feedstocks and point to technical challenges requiring further R&D.

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 categoriesMeta-epidemiology (narrow)
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 score0.419
Threshold uncertainty score1.000

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 head0.019
GPT teacher head0.214
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