Microbial community in anoxic–oxic–settling–anaerobic sludge reduction process revealed by 454 pyrosequencing analysis
Bibliographic record
Abstract
Modification of the anoxic-oxic (AO) process by inserting a sludge holding tank (SHT) into the sludge return line forms an anoxic-oxic-settling-anaerobic (A+OSA) process that can achieve a 48.98% sludge reduction rate. The 454 pyrosequencing method was used to obtain the microbial communities of the AO and A+OSA processes. Results showed that the microbial community structures of the 2 processes were different as a result of the SHT insertion. Bacteria assigned to the phyla Proteobacteria and Bacteroidetes commonly existed and dominated the microbial populations of the 2 processes. However, the relative abundance of these populations shifted in the presence of SHT. The relative abundance of Proteobacteria decreased during the A+OSA process. A specific comparison at the class level showed that Sphingobacteria was enriched in the A+OSA process. The result suggested that the fermentative bacteria Sphingobacteria may have key functions in reducing the sludge from the A+OSA process. Uncultured Nitrosomonadaceae gradually became the dominant ammonia-oxidizing bacteria, and the nitrite-oxidizing bacterium Nitrospira was enriched in the A+OSA process. Both occurrences were favorable for stabilized nitrogen removal. The known denitrifying species in the A+OSA process were similar to those in the AO process; however, their relative abundance also decreased.
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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 | 0.001 | 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 | 0.000 |
| 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".