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Record W39485822 · doi:10.2166/wqrj.2003.018

Innovative Biological Treatment Processes for Wastewater in Canada

2003· article· en· W39485822 on OpenAlexaffabout
Catherine N. Mulligan, Bernard F. Gibbs

Bibliographic record

VenueWater Quality Research Journal · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsConcordia University
Fundersnot available
KeywordsBiofilterUltrafiltration (renal)WastewaterEnvironmental scienceBiosorptionWaste managementSuspended solidsBioreactorSewage treatmentBiochemical engineeringEnvironmental engineeringChemistryEngineeringAdsorption

Abstract

fetched live from OpenAlex

Abstract Biological treatment of wastewater has been employed successfully for many types of industries. Aerobic processes have been used extensively. Production of large amounts of sludge is the main problem and methods such as biofilters and membrane bioreactors are being developed to combat this phenomenon. Anaerobic waste treatment has undergone significant developments and is now reliable with low retention times. The UASB, the original high rate anaerobic reactor, is now becoming less popular than the EGSB reactor. New developments such as the Annamox process are highly promising for nitrogen removal. For metal removal, processes such as biosorption and biosurfactants combined with ultrafiltration membranes are under development. Biosurfactants have also shown promise as dispersing agents for oil spills. If space is available, wetlands can be used to reduce biological oxygen demand (BOD), total suspended solids (TSS), nutrients and heavy metals. These innovative processes are described in this paper in terms of applications, the stage of development, and future research needs particular to Canada.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.172
GPT teacher head0.372
Teacher spread0.200 · 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.

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

Citations12
Published2003
Admission routes2
Has abstractyes

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