MétaCan
Menu
Back to cohort
Record W4229489514 · doi:10.1680/jees.2013.0041

Spatial distribution of ammonia-oxidizing bacteria in the biofilm and suspended growth biomass of the full- and partial-bed biological aerated filters

2013· article· en· W4229489514 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsAerationOxidizing agentAmmoniaNitriteNitrificationBiofilmBacteriaEnvironmental chemistryBiomass (ecology)NitrogenBioreactorChemistryPulp and paper industryNitrateBiologyEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

The extent of comparable nitrogen removal in the full- and partial-bed biological aerated reactors needs further microbiological evidence, specifically the existence of ammonia-oxidizing bacteria (AOB). The nitrogen removal process in such systems is typically initiated by chemoliautotrophic ammonia-oxidizing bacteria converting ammonia to nitrite and traces of oxidized nitrogen gases. The formation of a dense biofilm as a result of higher turbulence would account for the higher number of AOB cells enumerated in the biofilm samples from the partial-bed reactor (4·3 × 105 ± 1·9 × 105 No. of AOB cells/mL sample) as compared with those from the full-bed reactor (1·5 × 105 ± 8·0 × 104 No. of AOB cells/mL sample).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.174
Teacher spread0.167 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207