Fermentation of glucose and starch particles using an inexpensive medium
Bibliographic record
Abstract
Abstract BACKGROUND: In North America, compared with sugar, starch is a more economical raw material to produce ethanol. Recently low temperature processing of starch granules directly to ethanol has been commercialized. With a view to minimizing process costs while maintaining a satisfactory ethanol yield, an inexpensive medium that does not require the addition of commercial α‐amylase has been developed for low temperature, direct fermentation of starch particles. RESULTS: Compared with an expensive medium that contained both yeast extract and α‐amylase, the inexpensive medium resulted in identical and 10% higher ethanol yields using glucose and starch granules as the raw substrates, respectively, but required longer processing times. Based on the different ingredient prices for expensive and inexpensive media, the operating cost to produce 10 million litres per year of ethanol utilizing simultaneous raw starch hydrolysis and fermentation is reduced over $ 2 million (Cdn) per year when using inexpensive medium. CONCLUSION: The new inexpensive medium and simultaneous raw starch hydrolysis and fermentation is considered the best strategy to produce ethanol directly from starch particles in North America. Copyright © 2009 Society of Chemical Industry
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".