Selection of Cyanobacteria Grown In Wastewater Treatment Systems for Metabolites Production
Bibliographic record
Abstract
Cyanobacteria (blue-green algae) are a photosynthetic procariotic group of microorganisms with a long adaptive and evolutionary diversification which has led to have a large and diverse array of metabolites with various bioactivities. In the last two decades the production of cyanobacteria has aroused special interest since they have been identified as one of the most promising group of organisms for the isolation of novel and biochemically active natural products. Unlike eukaryotic algae, cyanobacteria have the potential to assimilate and store compounds of interest such as glycogen and polyhydroxyalkanoates. Studies related to the production of cyanobacteria and their metabolites generally employ expensive pure or genetically modified cultures. An alternative approach for the production of cyanobacteria could be the use of wastewater-borne cyanobacteria cultures, using non-sterile waste streams as substrate. Wastewater treatment technologies are probably the most promising sustainable substitute to reduce additional production costs in cyanobacteria cultures. However, maintaining a dominant population of cyanobacteria in wastewater treatment systems is a challenging task. In this paper the factors determining the dominance of cyanobacteria are reviewed. Moreover, it is presented how to translate this knowledge on factors to cultures. Finally, strategies to obtain metabolites of interest are also reviewed.
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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 | 0.001 |
| 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 | 0.001 |
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".