Unveiling the factors shaping variability in biomass productivity: Meta-analysis of outdoor pilot-scale microalgal cultures
Bibliographic record
Abstract
• Assessed factors controlling productivity in 53 outdoor culture studies. • Productivity correlates negatively with culture depth, growth period, and salinity. • Inoculum, CO 2 , sulfate and growth period have potential to improve productivity. • Temperature, pH, and light have limited potential to improve productivity. Meta-analysis and machine learning is used to investigate factors influencing variation in biomass productivity in outdoor algal systems. Understanding these factors is essential for optimizing algal systems. Mean productivity across the analysed studies is 11 g m –2 day −1 , with 6 % of observations surpassing 25 g m –2 day −1 . Analysis reveals that algal species alone is insufficient to optimize productivity, as indicated by wide intra-species variation (5 to 42 g m –2 day −1 ). A negative linear relationship between productivity and culture depth (Pearson correlation coefficient, ρ=0.56), growth period (ρ=0.56), and media salinity (ρ=0.64) is identified. Other variables exhibit non-linear associations. Inoculum density, CO 2 content, sulfate concentration, and growth period can enhance productivity while temperature, pH, photosynthetically active radiation, and photoperiod show limited potential. Targeted optimization of key input variables offers a promising pathway to significantly boost productivity and accelerate the deployment of algal systems for sustainable bioproduction.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".