Globalizing the Postcolony: Contesting Discourses of Gender and Development in Francophone Africa
Bibliographic record
Abstract
The conversion of lignocellulosic biomass to fuel ethanol typically involves a disruptive pretreatment process followed by enzyme-catalyzed hydrolysis of the cellulose and hemicellulose components to fermentable sugars. Attempts to improve process economics include protein engineering of cellulases, xylanases and related hydrolases to improve their specific activity or stability. However, it is recognized that enzyme performance is reduced during lignocellulose hydrolysis by interaction with lignin or lignin-carbohydrate complex (LCC), so the selection or engineering of enzymes with reduced lignin interaction offers an alternative means of enzyme improvement. This study examines the inhibition of seven cellulase preparations, three xylanase preparations and a beta-glucosidase preparation by two purified, particulate lignin preparations derived from softwood using an organosolv pretreatment process followed by enzymatic hydrolysis. The two lignin preparations had similar particle sizes and surface areas but differed significantly in other physical properties and in their chemical compositions determined by a 2D correlation HSQC NMR technique and quantitative 13C NMR spectroscopy. The various cellulases differed by up to 3.5-fold in their inhibition by lignin, while the xylanases showed less variability (< or = 1.7-fold). Of all the enzymes tested, beta-glucosidase was least affected by lignin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".