Changes in BNR Microbial Community in Response to Different Selection Pressure
Bibliographic record
Abstract
This study investigated structural changes in microbial community of biological nutrient removal (BNR) in response to changes in substrate composition (ammonium and phosphate), redox condition, and morphological characteristics (flocs to granules), with a focus on nitrification and phosphate removal. Analyzing treatment performance and 16S rRNA phylogenetic gene sequencing data suggested that heterotrophic nitrification (HN) and autotrophic nitrification (AN) potentially happened in aerobic organic-rich (HN_AS) and aerobic organic-deficient (AN_AS) activated sludge batch reactors, respectively. However, phosphate release and uptake were not observed under alternating anaerobic/aerobic regime. Phosphate release could not be induced even when anaerobic phase was extended, although Accumulibacter existed in the inoculum (5.1% of total bacteria). Some potential HN (e.g., Thauera, Acinetobacter, Flavobacterium), AN (e.g., Nitrosomonas (3.2%) and Nitrospira), and unconventional phosphate-accumulating organisms (PAOs) were identified. Putative HN bacteria (i.e., Thauera (29–36%) and Flavobacterium (18–25%)) were enriched in aerobic granular sludge (AGS) regardless of the granular reactor operation mode. Enrichment of HN organisms in the AGS was suspected to be mainly due to granulation, possibly due to the floc-forming ability of HN species. Thus, HN is likely to play a role in nitrogen removal in AGS reactors. This study is supposed to serve as a starting point for the investigation of the microbial communities of AS- and AGS-based BNR processes. It is recommended that the identified roles for the isolated bacteria are further investigated in future works.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".