Fast media optimization for mixotrophic cultivation of Chlorella vulgaris
Bibliographic record
Abstract
Microalgae can accumulate large proportions of their dry cell weight as storage lipids when grown under appropriate nutrient limiting conditions. While a high ratio of carbon to nitrogen is often cited as the primary mode of triggering lipid accumulation in microalgae, fast optimization strategies to increase lipid production for mixotrophic cultivation have been difficult to developed due to the low cell densities of algal cultures, and consequently the limited amount of biomass available for compositional analysis. Response surface methodologies provide a power tool for assessing complex relationships such as the interaction between the carbon source and nitrogen source. A 15 run Box-Behnken design performed in shaker flasks was effective in studying the effect of carbon, nitrogen, and magnesium on the growth rate, maximum cell density, lipid accumulation rate, and glucose consumption rate. Using end-point dry cell weight and total lipid content as assessed by direct transesterification to FAME, numerical optimization resulted in a significant increase in lipid content from 18.5 ± 0.76% to 37.6 ± 0.12% and a cell density of 5.3 ± 0.1 g/L to 6.1 ± 0.1 g/L between the centre point of the design and the optimized culture conditions. The presented optimization process required less than 2 weeks to complete, was simple, and resulted in an overall lipid productivity of 383 mg/L·d.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".