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Record W174942030 · doi:10.2166/wst.2005.0671

Performance comparison of a pilot-scale membrane bioreactor and a full-scale sequencing batch reactor with sand filtration: treatment of low strength wastewater from a northern Canadian Aboriginal Community

2005· article· en· W174942030 on OpenAlexaffabout
Kristinn Cameron Frederickson

Bibliographic record

VenueWater Science & Technology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEffluentWastewaterMembrane bioreactorSequencing batch reactorEnvironmental scienceFiltration (mathematics)Sewage treatmentBioreactorSuspended solidsEnvironmental engineeringWaste managementTotal suspended solidsPulp and paper industryChemistryEngineeringChemical oxygen demand

Abstract

fetched live from OpenAlex

Northern Aboriginal communities in Canada suffer from poor wastewater treatment. Treatment systems on 75% of Manitoban Aboriginal communities produce substandard effluent despite the presence of sophisticated treatment systems. A 200-litre, pilot-scale membrane bioreactor (MBR) was established on the Opaskwayak Cree Nation to investigate the feasibility of MBRs in mitigating Aboriginal wastewater treatment issues. The pilot system was remote controlled and monitored via the Internet using the program pcAnywhere. The community utilized two existing sequencing batch reactors (SBR) and three sand filters for wastewater treatment. The community wastewater was relatively weak and highly fluctuating which led to poorly settling sludge that readily fouled the sand filters. A comparison study between the MBR and SBR was undertaken from September to December 2003. Operated at a 10-hour hydraulic retention time and 20-day solids residence time, the MBR outperformed the SBR and sand filtration on BOD and suspended solids removal. Furthermore, the MBR showed high levels of nitrification despite relatively cold water temperatures.

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 score0.308
Threshold uncertainty score0.943

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 studies0.0000.002
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 head0.223
Teacher spread0.211 · 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

Citations3
Published2005
Admission routes2
Has abstractyes

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