Comparative sugar recovery and fermentation data following pretreatment of poplar wood by leading technologies
Bibliographic record
Abstract
Through a Biomass Refining Consortium for Applied Fundamentals and Innovation among Auburn University, Dartmouth College, Michigan State University, the National Renewable Energy Laboratory, Purdue University, Texas A&M University, the University of British Columbia, and the University of California at Riverside, leading pretreatment technologies based on ammonia fiber expansion, aqueous ammonia recycle, dilute sulfuric acid, lime, neutral pH, and sulfur dioxide were applied to a single source of poplar wood, and the remaining solids from each technology were hydrolyzed to sugars using the same enzymes. Identical analytical methods and a consistent material balance methodology were employed to develop comparative performance data for each combination of pretreatment and enzymes. Overall, compared to data with corn stover employed previously, the results showed that poplar was more recalcitrant to conversion to sugars and that sugar yields from the combined operations of pretreatment and enzymatic hydrolysis varied more among pretreatments. However, application of more severe pretreatment conditions gave good yields from sulfur dioxide and lime, and a recombinant yeast strain fermented the mixed stream of glucose and xylose sugars released by enzymatic hydrolysis of water washed solids from all pretreatments to ethanol with similarly high yields. An Agricultural and Industrial Advisory Board followed progress and helped steer the research to meet scientific and commercial needs.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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.
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".