Microalgae for phosphorus removal and biomass production: a six species screen for dual‐purpose organisms
Bibliographic record
Abstract
Abstract Microalgae biofuel production can be feasible when a second function is added, such as wastewater treatment. Microalgae differ in uptake of phosphorus (P) and growth, making top performer identification fundamental. The objective of this screen was to identify dual‐purpose candidates capable of high rates of P removal and growth. Three freshwater – Chlorella sp., Monoraphidium minutum sp., and Scenedesmus sp. – and three marine – Nannochloropsis sp., N. limnetica sp., and Tetraselmis suecica sp. – species were batch cultured in 250 mL flasks over 16 days to quantitate total phosphorus (TP) removal and growth as a function of P loads (control, and 5, 10, and 15 mg L−1 enrichment of control). Experimental design used 100 μmol m−2 s−1 of light, a light/dark cycle of 14/10 h, and no CO2 enrichment. Phosphorus uptake was dependent on species, duration of exposure, and treatment, with significant interaction effects. Growth was dependant on species and treatment. Not all species showed increased P removal with increasing P addition, and no species demonstrated higher growth. Nannochloropsis sp and N. limnetica sp. performed poorly across all treatments. Two dual‐purpose candidates were identified. At the 10 mg L−1 treatment Monoraphidium minutum sp. removed 67.1% (6.66 mg L−1 ± 0.60 SE) of TP at day 8, 79.3% (7.86 mg L−1 ± 0.28 SE) at day 16, and biomass accumulation of 0.63 g L−1 ± 0.06 SE at day 16. At the same treatment Tetraselmis suecica sp. removed 79.4% (6.98 mg L−1 ± 0.24 SE) TP at day 8, 83.0% (7.30 mg L−1 ± 0.60 SE) at day 16, and biomass of 0.55 g L−1 ± 0.02 SE at day 16. These species merit further study using high‐density wastewater cultures and lipid profiling to assess suitability for a nutrient removal and biomass/biofuel production scheme.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".