Bioremediation of wastewater containing azo dyes through sequential anaerobic–aerobic bioreactor system and its biodiversity
Bibliographic record
Abstract
Wide range of dyes and dyestuffs used in textile manufacturing are xenobiotic compounds and attract stricter to strict environmental regulations. The ability of microbial consortia to decolorize and metabolize dyes has long been known, and the use of bioremediation based technologies for treating textile wastewater has attracted interest. These dyes are decolorized by microbial consortia but technologies for their complete mineralization are still not developed. The most logical concept for the removal of azo dyes in biological wastewater treatment systems is based on anaerobic treatment, for the reductive decolorization, in combination with aerobic treatment, for the degradation of the by-products (aromatic amines) generated in the anaerobic bioreactor. Several research and review articles were published on anaerobic decolorization; however, research on complete mineralization of dyes through sequential anaerobic–aerobic bioreactors has received greater attention recently. Bioremediation through sequential anaerobic–aerobic bioreactor system has been reviewed in this article with critical appraisal using data generated through our experiments. While reviewing this work, we realized the importance of microbial diversity in a treatment unit to better understand the functional status to enhance the mineralization activity of the bioreactor.
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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) | 0.000 | 0.001 |
| 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".