A rapid microassay to evaluate enzymatic hydrolysis of lignocellulosic substrates
Bibliographic record
Abstract
Current attempts to produce ethanol from lignocellulosic biomass are focused on the optimization of pretreatment to reduce substrate recalcitrance and the improvement of enzymes for hydrolysis of the cellulose and hemicellulose components to produce fermentable sugars. Research aimed at optimizing both aspects of the bioconversion process involves assessment of the effects of multiple variables on enzyme efficiency, resulting in large factorial experiments with intensive assay requirements. A rapid assay for lignocellulose hydrolysis has been developed to address this need. Pretreated lignocellulose is formed into handsheets, which are then used to prepare small disks that are easily dispensed into microtiter plates. The hydrolysis of cellulose to glucose is estimated using an enzyme-coupled spectrophotometric assay. Using disks prepared from ethanol organosolv pretreated yellow poplar, it is shown that the assay generates data comparable with those produced by hydrolysis of pretreated yellow poplar pulp in Erlenmeyer flasks, followed by HPLC analysis of glucose. The assay shows considerable time and cost benefits over the standard assay protocol and is applicable to a broad range of lignocellulosic substrates.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".