The current strategies and parameters for the enhanced microbial production of 2,3-butanediol
Bibliographic record
Abstract
• 2,3-Butanediol (2,3-BD) is a propitious compound with many industrial uses. • 2,3-BD production has always been hampered by low fermentation yields and high production costs. • 2,3-BD production may be enhanced by optimization of culture conditions and use of high-producing strains. • Metabolic engineering tools are currently used to generate high-yielding strains. 2,3-Butanediol (2,3-BD) is a propitious compound with many industrial uses ranging from rubber, fuels, and cosmetics to food additives. Its microbial production has especially attracted as an alternative way to the petroleum-based production. However, 2,3-BD production has always been hampered by low yields and high production costs. The enhanced production of 2,3-butanediol requires screening of the best strains and a systematic optimization of fermentation conditions. Moreover, the metabolic pathway engineering is essential to achieve the best results and minimize the production costs by rendering the strains to use efficiently low cost substrates. This review is to provide up-to-date information on the current strategies and parameters for the enhanced microbial production of 2,3-BD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".