Acid‐catalyzed steam pretreatment of lodgepole pine and subsequent enzymatic hydrolysis and fermentation to ethanol
Bibliographic record
Abstract
Utilization of ethanol produced from biomass has the potential to offset the use of gasoline and reduce CO(2) emissions. This could reduce the effects of global warming, one of which is the current outbreak of epidemic proportions of the mountain pine beetle (MPB) in British Columbia (BC), Canada. The result of this is increasing volumes of dead lodgepole pine with increasingly limited commercial uses. Bioconversion of lodgepole pine to ethanol using SO(2)-catalyzed steam explosion was investigated. The optimum pretreatment condition for this feedstock was determined to be 200 degrees C, 5 min, and 4% SO(2) (w/w). Simultaneous saccharification and fermentation (SSF) of this material provided an overall ethanol yield of 77% of the theoretical yield from raw material based on starting glucan, mannan, and galactan, which corresponds to 244 g ethanol/kg raw material within 30 h. Three conditions representing low (L), medium (M), and high (H) severity were also applied to healthy lodgepole pine. Although the M severity conditions of 200 degrees C, 5 min, and 4% SO(2) were sufficiently robust to pretreat healthy wood, the substrate produced from beetle-killed (BK) wood provided consistently higher ethanol yields after SSF than the other substrates tested. BK lodgepole pine appears to be an excellent candidate for efficient and productive bioconversion to ethanol.
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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 | 0.000 |
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".