Bacterial Community and “ <i>Candidatus</i> Accumulibacter” Population Dynamics in Laboratory-Scale Enhanced Biological Phosphorus Removal Reactors
Bibliographic record
Abstract
"Candidatus Accumulibacter" and total bacterial community dynamics were studied in two lab-scale enhanced biological phosphorus removal (EBPR) reactors by using a community fingerprint technique, automated ribosomal intergenic spacer analysis (ARISA). We first evaluated the quantitative capability of ARISA compared to quantitative real-time PCR (qPCR). ARISA and qPCR provided comparable relative quantification of the two dominant "Ca. Accumulibacter" clades (IA and IIA) detected in our reactors. The quantification of total "Ca. Accumulibacter" 16S rRNA genes relative to that from the total bacterial community was highly correlated, with ARISA systematically underestimating "Ca. Accumulibacter" abundance, probably due to the different normalization techniques applied. During 6 months of normal (undisturbed) operation, the distribution of the two clades within the total "Ca. Accumulibacter" population was quite stable in one reactor while comparatively dynamic in the other reactor. However, the variance in the clade distribution did not appear to affect reactor performance. Instead, good EBPR activity was positively associated with the abundance of total "Ca. Accumulibacter." Therefore, we concluded that the different clades in the system provided functional redundancy. We disturbed the reactor operation by adding nitrate together with acetate feeding in the anaerobic phase to reach initial reactor concentrations of 10 mg/liter NO(3)-N for 35 days. The reactor performance deteriorated with a concomitant decrease in the total "Ca. Accumulibacter" population, suggesting that a population shift was the cause of performance upset after a long exposure to nitrate in the anaerobic phase.
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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) | 0.001 | 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) | 0.000 | 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".