MétaCan
Menu
Back to cohort
Record W2278236305 · doi:10.2166/wst.2016.057

Nitrification and denitrifying phosphorus removal in an upright continuous flow reactor

2016· article· en· W2278236305 on OpenAlexafffund
Maryam Reza, Manuel Álvarez Cuenca

Bibliographic record

VenueWater Science & Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersUniversity of Toronto
KeywordsDenitrifying bacteriaNitrificationBioreactorPhosphorusEnhanced biological phosphorus removalAnoxic watersActivated sludgeNutrientSequencing batch reactorChemistryEnvironmental chemistryEnvironmental engineeringPulp and paper industryBiomass (ecology)DenitrificationNitrogenEnvironmental scienceSewage treatmentEcologyBiology

Abstract

fetched live from OpenAlex

Simultaneous nitrification and denitrifying phosphorus removal was achieved in a single-sludge continuous flow bioreactor. The upright bioreactor was aligned with a biomass fermenter (BF) and operated continuously for over 350 days. This study revealed that unknown bacteria of the Saprospiraceae class may have been responsible for the successful nutrient removal in this bioreactor. The successive anoxic-aerobic stages of the bioreactor with upright alignment along with a 60 L BF created a unique ecosystem for the growth of nitrifier, denitrifiers, phosphorus accumulating organisms and denitrifying phosphorus accumulating organisms. Furthermore, total nitrogen to chemical oxygen demand (COD) ratio and total phosphorus to COD ratio of 0.6 and 0.034, respectively, confirmed the comparative advantages of this advanced nutrient removal process relative to both sequencing batch reactors and activated sludge processes. The process yielded 95% nitrogen removal and over 90% phosphorus removal efficiencies.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.218
Teacher spread0.208 · 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

Citations13
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueWater Science & TechnologySame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207