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Record W280080231 · doi:10.2175/106143008x274275

Effects of Fluctuating Iron Dosage on Nitrification in Integrated Fixed Film and Conventional Activated Sludge Processes

2009· article· en· W280080231 on OpenAlexaffabout
Anne‐Emmanuelle Stricker, Lori Lishman, Ashley Barrie

Bibliographic record

VenueWater Environment Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsBrampton Civic HospitalEnvironment and Climate Change Canada
Fundersnot available
KeywordsNitrificationActivated sludgeEnvironmental engineeringSewage treatmentEnvironmental scienceWastewaterChemistryPulp and paper industryWaste managementNitrogenEngineering

Abstract

fetched live from OpenAlex

An integrated fixed-film activated sludge (IFFAS) process with four media cells is operating in parallel with a conventional activated sludge (CAS) train at Lakeview Wastewater Treatment Plant (Ontario, Canada). During winter 2007, an intensive sampling campaign was conducted to monitor the temporal and spatial variations of the nitrification capacity within the two plug-flow reactors. At the beginning of the six-week study, the CAS train was partially nitrifying, whereas the IFFAS train was nitrifying completely using the first two IFFAS cells only. Within one week, the CAS train lost nitrification because of a drop in solids retention time and pH caused by the onset of iron overdosing. When the IFFAS train received an iron spike, the carriers at the injection point (first cell) became iron-coated and lost 80% of their nitrification capacity. However, this train maintained its total nitrification capacity using the reserve capacity in the three remaining cells.

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 score0.100
Threshold uncertainty score0.666

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)0.0010.000

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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