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Record W2076973736 · doi:10.1002/bit.10159

Fast and efficient alkaline peroxide treatment to enhance the enzymatic digestibility of steam‐exploded softwood substrates

2002· article· en· W2076973736 on OpenAlex

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

VenueBiotechnology and Bioengineering · 2002
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsWestern Forest ProductsUniversity of British Columbia
Fundersnot available
KeywordsSoftwoodSteam explosionLigninChemistryCelluloseHydrogen peroxideHardwoodHydrolysisPulp and paper industryEnzymatic hydrolysisPeroxideSugarCellulaseOrganic chemistryNuclear chemistryChromatographyBotany

Abstract

fetched live from OpenAlex

The enzymatic digestibility of steam-exploded Douglas-fir wood chips (steam exploded at 195 degrees C, 4.5 min, and 4.5% (w/w) SO(2)) was significantly improved using an optimized alkaline peroxide treatment. Best hydrolysis yields were attained when the steam-exploded material was post-treated with 1% hydrogen peroxide at pH 11.5 and 80 degrees C for 45 min. This alkaline peroxide treatment was applied directly to the water-washed, steam-exploded material eliminating the need for independent alkali treatment with 0.4% NaOH, which has been traditionally used to post-treat wood samples to try to remove residual lignin. Approximately 90% of the lignin in the original wood was solubilized by this novel procedure, leaving a cellulose-rich residue that was completely hydrolyzed within 48 h, using an enzyme loading of 10 FPU/g cellulose. About 82% of the originally available polysaccharide components of the wood could be recovered. The 18% of the carbohydrate that was not recovered was lost primarily to sugar degradation during steam explosion.

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 score0.047
Threshold uncertainty score0.433

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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