Bioprospecting Native Oleaginous Microalgae for Wastewater Nutrient Remediation and Lipid Production: An Environmentally Sustainable Approach
Bibliographic record
Abstract
Subtropical climate in Florida offers a unique opportunity for sustainable biofuel production using native microalgae cultivated in untreated wastewater. This study bioprospected oleaginous microalgal consortia from a wastewater holding tank at the Thomas P. Smith Water Reclamation Facility in Tallahassee, Florida, aiming to identify strains capable of both nutrient remediation and lipid accumulation. Using Fluorescence-Activated Cell Sorting (FACS), chlorophyll-containing cells were isolated and cultured on BG-11 media. Shotgun metagenomics revealed that the most robust consortia—labeled C3, C4, and C9—were dominated by Chlamydomonas, Acutodesmus, and Volvox spp., alongside diverse bacterial, fungal, and archaeal communities. Functional gene analysis indicated active pathways for photosynthesis, lipid biosynthesis, and nutrient assimilation. In microcosm experiments, these consortia achieved up to 100% ammonia, 95% phosphorus, and 89% nitrate removal, outperforming control treatments. Lipid screening confirmed significant accumulation, with consortium C9 showing the highest yield. These findings underscore the potential of native microalgal consortia for integrated wastewater treatment and biofuel production, advancing circular bioeconomy strategies for subtropical regions.
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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 | 0.000 | 0.000 |
| 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".