Impact of pretreatment severity on fungal cellulase production on sugarcane bagasse substrate
Bibliographic record
Abstract
On-site production of cellulases using lignocellulosic materials can improve the economic viability of biorefineries. This, however, requires the pretreatment of substrates using thermochemical conditions that can vary in severity. To understand the effect of pretreatment severity on cellulase production by Aspergillus ustus S3 on sugarcane bagasse, we applied NaOH pretreatment corresponding to 3 severity factors (SF1.32, SF1.79, and SF3.64) to generate SCB that was used as inducing substrate. The highest cellulase activity (0.681 U/mL) was obtained with the intermediate severity (SF1.79) while significantly lower activities of 0.495 and 0.539 U/mL were recorded with low (SF1.32) and high (SF3.64) severities, respectively. Chemical and structural characterization revealed that low and intermediate severities improved cellulose accessibility and cellulase titres while high severity impaired them, thus limiting substrate suitability for enzyme production. These results show that though high SFs might be desirable in other applications, moderate severities may be more appropriate for cellulase production.
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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) | 0.001 | 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 | 0.001 |
| 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".