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Record W2317187670 · doi:10.2166/wqrjc.2013.046

Characterisation of retention tank water quality: particle settling velocity distribution and retention time

2013· article· en· W2317187670 on OpenAlexaff
Thibaud Maruéjouls, Peter A. Vanrolleghem, Geneviève Pelletier, Paul Lessard

Bibliographic record

VenueWater Quality Research Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSettlingHydraulic retention timeContext (archaeology)InletPollutantWastewaterEnvironmental scienceChemical oxygen demandWater qualityEnvironmental engineeringCombined sewerParticle (ecology)Total suspended solidsRetention timeChemistryEngineeringSurface runoffGeologyEcologyStormwater

Abstract

fetched live from OpenAlex

Retention tanks (RTs) are commonly used to reduce combined sewer overflows, management of which is an important way of reducing the impacts of urban development on receiving waters. However, overflow characteristics and the processes affecting them are not yet fully understood. In a context of integrated urban wastewater systems, the management of RTs is mainly done to satisfy hydraulic constraints even if the idea behind such structures is to limit the discharge of pollutants to the environment. This study reports new insights in the settling processes and the pollutant behaviour occurring in an off-line RT. The authors first focus on the total suspended solids (TSS) and the total chemical oxygen demand (CODt) dynamics at the inlet and the outlet of a RT. Secondly, they focus on the possible relationship between the variation of the particle settling velocity distribution of particles and the TSS concentration dynamics. Finally, analyses of the TSS and CODt concentration evolution during tank emptying give information on the interaction between wastewater retention time and the settling performance.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.111
GPT teacher head0.341
Teacher spread0.230 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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