Synergistic effects of soybean oligosaccharides and Chlorella pyrenoidosa on water quality and microbial community structure in biofloc system
Bibliographic record
Abstract
Abstract This study explored the synergistic effects of soybean oligosaccharides (SBOS) and Chlorella pyrenoidosa on water quality and microbial community structure of aquaculture effluent in a biofloc system. Five experimental treatments were designed including CON group (control, no treatment), GLU group (glucose), SBOS group (2.5% SBOS and 97.5% glucose), CP group (glucose and Chlorella pyrenoidosa), and CS group (2.5% SBOS, 97.5% glucose and Chlorella pyrenoidosa). Chlorella pyrenoidosa were included at a concentration of 5.37×10⁵ cells/mL for selected treatments. Each treatment had 5 replicates, and C/N ratio was 15. Over the nine-day experimental period, combination of SBOS and Chlorella pyrenoidosa demonstrated significant synergistic effects on water quality improvement and biofloc formation (P < 0.05). Specifically, in CS group, nitrate concentrations were significantly reduced on day 1, nitrite nitrogen concentrations exhibited a marked reduction on day 5, and both nitrite and total nitrogen concentrations showed significant reductions on day 7 compared to GLU group (P < 0.05). Biofloc volume (FV) in CS group showed a significant increase on day 3 compared to both CON and GLU groups (P < 0.05). Turbidity (NTU) was significantly lower in all experimental groups compared to CON group (P < 0.05). Compared with other experimental groups, the abundance of Aeromonasaceae decreased significantly, and Bacillus and Mycobacteria increased significantly in CS group, which contributed to the enhanced nitrogen cycling and degradation of organic matter degradation pathway (P < 0.05). These findings suggest that SBOS and Chlorella pyrenoidosa exhibit synergistic effects in treatment of effluent in biofloc system, and efficiently remove nitrogen and optimize microbial community structure.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".