Nitrification in activated sludge batch reactors is linked to protozoan grazing of the bacterial population
Bibliographic record
Abstract
Protozoa feed upon free-swimming bacteria and suspended particles inducing flocculation and increasing the turnover rate of nutrients in complex mixed communities. In this study, the effect of protozoan grazing on nitrification was examined in activated sludge in batch cultures maintained over a 14-day period. A reduction in the protozoan grazing pressure was accomplished by using either a dilution series or the protozoan inhibitor cycloheximide. As the dilutions increased, the nitrification rate showed a decline, suggesting that a reduction in protozoan or bacterial concentration may cause a decrease in nitrification potential. In the presence of cycloheximide, where the bacterial concentration was not altered, the rates of production of ammonia, nitrite, and nitrate all were significantly lower in the absence of active protozoans. These results suggest that a reduction in the number or activity of the protozoans reduces nitrification, possibly by limiting the availability of nutrients for slow-growing ammonia and nitrite oxidizers through excretion products. Furthermore, the ability of protozoans to groom the heterotrophic bacterial population in such systems may also play a role in reducing interspecies competition for nitrification substrates and thereby augment 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 | 0.000 | 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 | 0.000 | 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".