Screening of ligninolytic fungi for biological pretreatment of lignocellulosic biomass
Bibliographic record
Abstract
To identify white rot fungi with high potential in biological pretreatment of lignocellulosic biomass, preliminary screening was carried out on plates by testing different strains for their ability to oxidize guaiacol and decolorize the dyes azure B and Poly R-478. Of the 86 strains screened, 16 were selected for secondary screening for their ligninolytic ability; however, low manganese peroxidase activity and no lignin peroxidase activity were detected. Strain BBEL0970 proved to be the most efficient in laccase production and was subsequently identified as Trametes versicolor by analysis of the ribosomal DNA internal transcribed spacer gene sequence. In combining laccase production with biological pretreatment, the replacement of glucose with barley straw significantly improved the laccase activity by up to 10.3 U/mL, which provided evidence toward potential utilization of barley straw in laccase production by BBEL0970. Simultaneously, comparison by thermogravimetric analysis of the untreated and pretreated barley straw in liquid fermentation of laccase also demonstrated the high potential of BBEL0970 in biological pretreatment of lignocellulosic biomass. This work sheds light on further exploration on the integrated process of low-cost laccase production and efficient biological pretreatment of barley straw by T. versicolor BBEL0970.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".