Nutrient screening of Chlorella vulgaris and C. variabilis using high-throughput biolog phenotype arrays
Bibliographic record
Abstract
Microalgae , particularly Chlorella species, are versatile microorganisms with significant scientific potential in various domains, including recombinant protein production, wastewater treatment , biofuel production, bio-fertilizers, food source, pharmaceuticals, and carbon capture. However, conventional growth media have often been the default choice for Chlorella cultivation. This study utilizes Biolog phenotype array plates to explore the growth responses of Chlorella vulgaris and Chlorella variabilis to a broad spectrum of carbon, nitrogen, phosphorus, and sulfur sources. The growth dynamics were captured by integrating the area under the OD-time curve. The results revealed growth preferences for both Chlorella species, emphasizing their unique nutrient source requirements and illuminated some unexpected growth behaviors. C. vulgaris exhibited equal preference for trehalose as glucose, and C. variabilis was unable to metabolize nitrate or sucrose, two staples of modified BBM media most commonly used for its cultivation. These findings contribute to a deeper understanding of the metabolic capacities of C. vulgaris and C. variabilis , informing potentially more efficient and tailored microalgal cultivation practices across diverse applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".