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Record W2141222247 · doi:10.1139/l2012-110

Bioretention cell efficacy in cold climates: Part 1 — hydrologic performance

2012· article· en· W2141222247 on OpenAlexafffundvenue
Usman T. Khan, Caterina Valeo, Angus Chu, Bert van Duin

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of VictoriaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsBioretentionSurface runoffEffluentEnvironmental scienceStormwaterLow-impact developmentHydraulic conductivityEnvironmental engineeringHydrology (agriculture)Soil waterSoil scienceStormwater managementGeotechnical engineeringEngineeringEcology

Abstract

fetched live from OpenAlex

Bioretention cells are an emerging low impact development technology that address urban stormwater runoff concerns. Field and column experiments were conducted to assess the efficacy of bioretention cells in cold conditions. Field experiments in a prairie environment demonstrated a significant decrease (91.5%) in effluent volumes compared to influent volumes. The majority (∼60%) of the runoff percolated to the surrounding soils or evapotranspirated. Cold condition performance significantly impacted high volume events and was characterized by significantly higher effluent volumes, significantly lower runoff storage, higher effluent peak flow rates, and longer peak delays. A partially frozen surface layer caused the changes in performance. Long-term simulation experiments on the columns indicated a significant decrease in saturated hydraulic conductivity over the first 4 equivalent years of operation, before levelling to a constant value.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.166
Teacher spread0.155 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations66
Published2012
Admission routes3
Has abstractyes

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