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Record W108869445 · doi:10.2166/wst.2005.0035

High-rate stormwater clarification with polymeric flocculant addition

2005· article· en· W108869445 on OpenAlexafffundabout
John A. Wood, Conghui He, Quintin Rochfort, Jiří Maršálek, Peter Seto, Ming-jia Yang, P. Chessie, Sandra Kok

Bibliographic record

VenueWater Science & Technology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsToronto Public HealthEnvironment and Climate Change Canada
FundersGovernment of Canada
KeywordsClarifierStormwaterFlocculationEffluentSuspended solidsSurface runoffEnvironmental scienceEnvironmental engineeringWaste managementWastewaterEngineering

Abstract

fetched live from OpenAlex

Treatment of urban stormwater by clarification, with flocculant addition, was studied in Toronto, Canada using a pilot-scale clarifier with removable lamellar plates. Almost 90 stormwater runoff events were characterised at the study site and found fairly polluted. The previous research phase indicated good treatability of this stormwater by lamellar clarification with flocculant addition (total suspended solids, TSS, removal of 84%, at a surface load of 15 m/h), but there were concerns about cleaning plates after storm events. With the aid of numerical modelling, hydraulic improvements to the clarifier inlet zone were retrofitted in 2004 and permitted the removal of the lamellar pack without a loss in treatment efficiency. In the modified clarifier, a cationic polymeric flocculant dosage of 4 mg/L with conventional clarification provided a TSS removal of 77%, at surface loads up to 43 m/h. The use of the polymer did not increase the acute toxicity of the treated effluent. The clarifier sludge was severely polluted by several heavy metals and would require special disposal. The treatment process tested could be well applied in projects requiring intensive stormwater treatment at compact sites.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.134
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.005
GPT teacher head0.181
Teacher spread0.177 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2005
Admission routes3
Has abstractyes

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