Optimization of Conditions for Petroleum-degrading Bacteria CQ6 Producing Biosurfactant
Bibliographic record
Abstract
In order to determine the surfactant-producing situation of petroleum-degradation bacteria in Changqing oil field,degradation rate of petroleum and surface tension of fermentation broth were studied when stain CQ-6 used crude oil as the carbon source, and the fermentation condition was optimized. Based on the single factor experiment, the temperature, the rotate speed and initial amount of oil as factors, the prediction model of quadratic regression polynomial equation on surfactantproducing was simulated with Box-Benhnken central composite principle and response surface methodology. The result showed that,the optimum condition of biosurfactant producing by strain CQ6 was as follows : the temperature was 25 ℃,the rotate speed was 190 r / min and the initial amount of oil was 3.4%.Under this condition, the degradation rate of petroleum increased from 64.4% to 80.2% and the surface tension of fermentation broth decreased from 32.5 m N / m to 27.0 m N / m compared with before optimization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".