Bioprocessing of Crop Residues using Fibrolytic Enzymes and Flavobacterium bolustinum for Enriching Animal Feed
Bibliographic record
Abstract
Flavobacterium bolustinum and its extracellular cellulase were tested for animal feed pretreatment.The fibrolytic enzymes, cellulase and pectinase were applied to various crop residues such as wheat straw, rice straw, corn seeds and sorghum for enriching animal feed.Different parameters like temperature, incubation time and enzyme dose had been optimized for maximum reducing sugar and protein release.The highest amount of reducing sugar obtained was 29.83 mg g -1 dry substrate and soluble protein was 27.34 mg g -1 dry substrate on single cellulase enzyme treatment at 50°C for 6 h.An increase in amount of released reducing sugar (39.5 mg g -1 dry substrate) and protein (33.88 mg g -1 dry substrate) was observed when enzyme cocktail (cellulose and pectinase) was used.Solid state fermentation using F. bolustinum had also been performed for all crop residues.It released higher amount of reducing sugar (41.36 mg g -1 ) and protein (47.21 mg g -1 ) as compared to enzymatic treatment.Different substrates resulted in appreciable weight loss by enzymatic treatment (15-35%) as well as fermentation using F. bolustinum (40%).Liquefaction of lignocellulosic rich crop residues, for better utilization of feed has never been reported earlier.
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 | 0.000 |
| 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 | 0.000 |
| 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 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".