Cellulase extraction from <i>Pseudomonas fluorescens</i> for efficient enzymatic hydrolysis and fermentation with <i>Pichia fermentans</i> and <i>Saccharomyces cerevisiae</i> for cellulosic bioethanol production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cellulase enzymes of Pseudomonas fluorescens were extracted for the efficient Enzymatic hydrolysis of horticultural plant wastes. Horticultural plant rejects (shoots and leaves) of Tuber plants like Taro, giant Taro, Elephant yam and Potato were used as lignocellulosic substrates. These are allowed for Physical treatment, chemical treatment and enzymatic treatment, by following SSCF process for bioethanol production. In the enzymatic treatment, cellulolytic organisms ( Pseudomonas fluorescens) were used to produce cellulase enzymes for the conversion of polymers into monomers from horticultural plant rejects. Saccharomyces cerevisiae and Pichia Fermentans were used along with cellulase enzymes in the SSCF process. Both of these organisms belong to the Saccharomycetaceae family. The lignocellulosic materials (Cellulose and Hemicellulose) have both hexose and pentose sugars, but Saccharomyces cerevisiae can ferment only hexose sugars. Cellulose has only hexose sugars but hemicellulose has both hexose and Pentose sugars. To ferment pentose sugars Pichia fermentans were explored here to get high ethanol yield. The use of Pichia fermentans was very efficient in fermenting pentose sugars and hence high yield was obtained. The cellulase extracted from Pseudomonas fluorescens are highly efficient in yielding more monomers from polymers and hence high ethanol was obtained on co fermentation
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 it