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Record W2149683972 · doi:10.3390/w4040995

Winter Performance of Inter-Locking Pavers—Stormwater Quantity and Quality

2012· article· en· W2149683972 on OpenAlexafffundabout
Jian Huang, Caterina Valeo, Jianxun He, Angus Chu

Bibliographic record

VenueWater · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsLakehead UniversityUniversity of VictoriaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Connecticut
KeywordsStormwaterSurface runoffEnvironmental scienceInfiltration (HVAC)Water qualityTotal suspended solidsEnvironmental engineeringHydrology (agriculture)Suspended solidsLow-impact developmentStormwater managementGeotechnical engineeringWastewaterEngineeringMeteorologyChemical oxygen demandGeography

Abstract

fetched live from OpenAlex

This study examined the effectiveness of open-joint inter-locking pavers in a permeable pavement in cold (winter) conditions. A field-scale inter-locking paver cell (UNI Eco-Optiloc®) was built to evaluate the hydraulic performance and water quality improvements experienced during freeze-thaw and frozen conditions in Calgary, Alberta, Canada. Hydraulic performance was assessed using stormwater runoff reduction (peaks and volumes) and surface infiltration capacity. Water quality performance for removal of total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP) and three heavy metals: copper, lead and zinc, was assessed. Results from the study demonstrated that the inter-locking pavers were effective in attenuating stormwater runoff peak volumes. The surface infiltration capacity decreased significantly due to the deposition of sanding and de-icing materials on the pavement surface during winter operation. Infiltrated stormwater was stored and treated within the pavement structure, which showed removal rates of 91% for TSS, 78% for TP, 6% for TN, 68% for zinc, 69% for copper and 55% for lead.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.243
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; both teacher heads agree on what is shown here.

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

Citations32
Published2012
Admission routes3
Has abstractyes

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