Feasibility of a microalgal wastewater treatment for the removal of nutrients under non‐sterile conditions and carbon limitation
Bibliographic record
Abstract
Abstract Microalgal treatment of municipal wastewater has been discussed as a novel strategy for the removal of excess nutrients and metals. Additionally, a number of products can be obtained from the resulting microalgal biomass, including energy products that can be utilized within the treatment plant. However, the effectiveness of these microalgal systems can be significantly affected by the natural biota, which could consequently impact the quality of the wastewater effluent. This study evaluated the performance of two microalgal species in the removal of nutrients from non‐sterile, highly concentrated synthetic wastewater. The results showed that monocultures of Scenedesmus sp. AMDD and Chlorella sorokiniana could remove up to 60 % NH4+, and 44 and 35 % PO43‐, respectively, in a semi‐continuous cultivation mode without negatively affecting effluent quality.
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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) | 0.000 | 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 | 0.000 |
| 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".