Enzymatic Hydrolysis of Steam-Exploded and Ethanol Organosolv-Pretreated Douglas-Fir by Novel and Commercial Fungal Cellulases
Bibliographic record
Abstract
Softwood residues are the most abundant feedstock available for bioconversion in many northern countries. However, the high costs for delignification and enzymatic hydrolysis currently deter commercialization of softwood bioconversion processes. This study evaluates the abilities of two novel fungal preparations (MSUBC1 and MSUBC2) and two commercial cellulase preparations (TR1 and TR2) to hydrolyze cellulose in Douglas-fir pretreated by steam explosion or ethanol organosolv process. MSUBC1 showed significantly better performance than the other preparations on both lignocellulosic substrates. In particular, MSUBC1 achieved >76% cellulose conversion for hydrolysis of steam-exploded Douglas-fir (∼44% lignin) after 72 h at low enzyme loading (10 filter paper units/g of cellulose) and without β-glucosidase supplementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".