Saccharification of agricultural residues by Streptomyces sp. and ethanol production from agro-waste mixture hydrolysate
Bibliographic record
Abstract
The present work demonstrated the potential of different agro-waste mixtures to produce ethanol. The forest soil bacterium (Streptomyces sp.) was exploited for the saccharification of the agro-waste mixture formulated by extreme vertices mixture design and the hydrolysate produced by saccharification was used for fermentation. The best formulation contained 43.33% orange peel, 33.33 % pumpkin pulp+seeds, and 23.33% pomegranate peel which exhibited significantly high reducing sugar (22.36±0.54 mg/g dry weight) among all other mixtures. The hydrolysate of this mixture when supplemented with 2% w/v fructose produced a maximum of 7.86±0.08% v/v ethanol by the yeast isolated from the brewer’s spent grains. Thus, easily available waste could be a promising source for yeast isolation and feedstock for ethanol production. Further, this study aids to reduce the risk of health, and environmental pollution, and developing the economy.
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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 | 0.000 |
| 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".