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Record W2158068201 · doi:10.1680/jees.2013.0021

Simultaneous nitrification and denitrification in non-planted pilot-scale modified vertical flow constructed wetland system

2013· article· en· W2158068201 on OpenAlexvenueno aff
Zuxin Xu, Xiaoli Du, Sheng Wang

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsDenitrificationNitrificationWetlandNitrateConstructed wetlandEnvironmental scienceNitrogenEnvironmental chemistryEffluentEnvironmental engineeringWastewaterChemistryEcologyBiology

Abstract

fetched live from OpenAlex

Rural wastewater containing low chemical oxygen demand (COD), but relatively high concentration of total nitrogen comprised of dominant ammonia-nitrogen, was treated in two vertical flow wetland systems whose effluent were discharged intermittently. A substantial loss of total nitrogen (30·3%) was found in a modified wetland system, which introduced an air pipe to enhance oxygen transfer, whereas only a minor loss (17·9%) in the conventional wetland. The correlation between pH, nitrite and nitrate concentrations, ammonia-nitrogen and nitrate-nitrogen distribution along with the depth in the wetlands indicated that the removal of total nitrogen did not follow the classic nitrification and denitrification pathway in the modified wetland system. Through mass balance calculation and theoretical analysis, it was found that simultaneous nitrification and denitrification (SND) was responsible for the transformation of soluble nitrogen into gaseous nitrogen in the modified wetland, thereby causing the loss of total nitrogen. The results showed that SND can be stimulated in aerobic wetland systems and its occurrence may depend on the biofilm thickness, the hydraulic detention time, and the type and population of microorganisms present in the wetland systems.

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 score0.914
Threshold uncertainty score0.449

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 head0.003
GPT teacher head0.165
Teacher spread0.162 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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