Preliminary evaluation of biomass and lipid production potential of Nannochloropsis sp. under optimal conditions with different wastewater samples for Exopolymeric substances (EPS) production
Bibliographic record
Abstract
Wastewater is a source of toxic chemicals, heavy metals and various pathogenic microorganisms. Disposal of untreated wastes into the environment causes antibiotic resistance of organisms and various pollutions. An efficient method for treating wastewater, producing bioenergy and byproducts on a sustainable basis is to cultivate microalgae in wastewater. Microalgae has emerged as a desirable source for biofuel production because of high biomass and lipid productivity from wastewater sources. The main characteristics that make microalgae an exceptional source of renewable energy are their quick growing rate, effective carbon collecting technique. Extracellular Polymeric Substance (EPS) production is getting more attention as a result of high hydrocarbon biosynthesis. STP water from effluent plant, wastewater from biogas plant and wastewater from biodiesel production plant were collected from Centre for Waste Management. Reverse osmosis water was used as control. Analysis of wastewater sample showed that its parameters exceeded safer limits. The bioremediation potential of Nannochloropsis species was studied at different concentration of sample. Nannochloropsis species was grown under stress conditions over 25-day period for biomass production and characterization. Lipids extraction was determined by the Bligh and Dyer method. The maximum growth and lipid production by Nannochloropsis species was observed in biodiesel wastewater sample than sample of biogas wastewater. The maximum lipid yield was 0.06 g/l in 1:1 ratio of sample and media.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".