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Record W2058541354 · doi:10.1139/w05-069

Nitrification in activated sludge batch reactors is linked to protozoan grazing of the bacterial population

2005· article· en· W2058541354 on OpenAlexafffundvenue
Penny Petropoulos, Kimberley Gilbride

Bibliographic record

VenueCanadian Journal of Microbiology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNitrificationActivated sludgeBiologyNitriteNitratePopulationMicrobiologyNutrientEnvironmental chemistryFood scienceEcologyChemistryEnvironmental engineeringNitrogenSewage treatmentEnvironmental science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.209
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2005
Admission routes3
Has abstractyes

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