Nitrifying Genera in Activated Sludge May Influence Nitrification Rates
Bibliographic record
Abstract
Sequencing batch reactors were acclimated under aerobic and alternating anoxic/aerobic conditions. Greater nitrification rates in the alternating reactor were investigated by comparing environmental conditions. In the alternating reactor, pH, alkalinity, oxygen, and nitrite were higher at the onset of aerobic nitrification. Kinetic studies and batch tests, with biomass developed under aerobic and alternating conditions, revealed that these factors were insufficient to explain the divergent nitrification rates. Nitrifying genera vary in nitrification kinetics and sensitivity to environmental conditions. Nitrosospira and Nitrospira spp. could dominate in aerobic reactors, as they are adapted to low nitrite and oxygen conditions. Nitrosomonas and Nitrobacter spp. are better competitors with abundant substrates and have higher nitrite tolerance, so they could excel under alternating conditions. This theoretical explanation is consistent with the kinetics and environmental conditions in these reactors and argues for using alternating treatment, because the harsh conditions select for populations with inherently faster nitrification rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".