Mixotrophic Cultivation of an Algae-Bacteria Consortium in Aluminium Smelter Wastewaters (Quebec, Canada): High Nitrogen Concentration Increases Overall Lipid Production
Bibliographic record
Abstract
To produce energy for in-house use, an aluminium smelter in Québec launched a study of mixotrophic cultivation of microalgae in its wastewaters with the objective of having an algae production company set up operations on site. To maximize lipid productivity and maintain the biological integrity of the consortium, specific nutrients need to be added to aluminium smelter wastewaters to cultivate the selected algae-bacteria consortium. A 2 3 factorial design was used to determine the organic carbon, nitrogen and phosphate inputs needed. Data on biomass and lipid productivity, as well as a “consortium integrity index,” were analyzed using a multiple linear regression model. The highest biomass productivity (0.93 g/L/d) and lipid productivity (0.023 g/L/d) were obtained using the highest tested concentration in nitrogen (0.200 g/L) and the lowest tested concentration in phosphate (0.003 g/L). No significant effect of the organic carbon—tested in concentrations of 1.64 g/L, 2.64 g/L, and 3.64 g/L (glucose) and added in two increments on days 5 and 7—on productivity for a starting cell density of 5 million cells/L was detected. To achieve maximal lipid production, the results suggested that biomass productivity should be prioritized rather than lipid accumulation in the cells through nitrogen starvation. The stability and integrity of the cultured consortium have to be maintained through an appropriate balance of nutrients. A low phosphate concentration increases the stability of the consortium, although part of the variation cannot be explained by the model. Finally, an analysis of fatty acid profiles showed that different concentrations in nitrogen and phosphorus impact the proportion of C18:1(n-9) and other minor fatty acids.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".