Primary exploration of metabolic characteristics in Poria cocos fermentation system with adding botanical medicine factors
Bibliographic record
Abstract
Objective To study the metabolic characteristics of Poria cocos fermentation system.Methods Using the biomass yield and exopolysaccharide concentration as the main evaluation indices to choose the excellent Poria cocos strain and to determine the best carbon source and the best nitrogen source for Poria cocos fermentation.The effects of 12 botanical drug factors on the biosynthesis of Poria cocos exopolysaccharides and the growth of Poria cocos were detected by adding to Poria cocos fermentation system.Results Poria cocos ZK had more effects on promoting exopolysaccharides biosynthesis and Poria cocos growth.The best carbon source was 2% glucose mixed with 1% corn starch and the best nitrogen source was 0.5% yeast extract.During the 12 chosen botanical medicine factors,Huangqi,Tea-leaves,Malt,Sanqi,Gegen and Chuanxinlian showed excellent promotion for Poria cocos exopolysaccharides biosynthesis,while Huangqi,Tea-leaves,Chuanxinlian and Luxiancao showed remarkable increase in Poria cocos growth.However,Luxiancao inhibited Poria cocos exopolysaccharides biosynthesis or secretion,and Gouqi,Shanzha,Danshen,Qiancao,Lianqiao obviously suppressed Poria cocos growth.Conclusion Botanical medicine factors have certain regulating effects on Poria cocos exopolysaccharides biosynthesis and Poria cocos cell growth.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".