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Hydrologic Performance of Permeable Pavement as an Adaptive Measure in Urban Areas: Case Studies near Montreal, Canada

2019· article· en· W2949818887 on OpenAlexaffabout
Catherine Vaillancourt, Sophie Duchesne, Geneviève Pelletier

Bibliographic record

VenueJournal of Hydrologic Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversité LavalMinistry of Sustainable Development, Environment and the Fight against Climate ChangeMinistry of Environment
Fundersnot available
KeywordsOutfallEnvironmental scienceSurface runoffCombined sewerHydrology (agriculture)Infiltration (HVAC)Flooding (psychology)StormwaterVolume (thermodynamics)GeologyMeteorologyEnvironmental engineeringGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

The infiltration capacity of permeable interlocking concrete pavement (PICP) was characterized on five sites located in the greater Montreal area (Canada). Surface infiltration rates up to more than 20,000 mm/h were observed, even in winter at subzero temperatures (°C). At one of the five monitored sites, rainfall and flow at the outlet were monitored for 12 months. This monitoring revealed peak flow delays ranging from 4 min to 4 h 42 min and runoff reductions ranging from 26% to 98%, depending on the rainfall event. These rainfall and flow data were used to calibrate a PICP hydrologic model that was then used to quantify the impact of implementing PICP in four real urban watersheds. For an eight-year rainfall series, simulations showed a reduction in volume (65%) and duration (21%–48%) of overflows in the two combined sewer systems, a reduction in peak flow (6%–45%) and volume (mean 30%) at the outfall of the two separate systems, and a reduction in surface flooding duration (24%–81%) for the four sewer systems.

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 score0.023
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.011
GPT teacher head0.193
Teacher spread0.182 · 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

Citations30
Published2019
Admission routes2
Has abstractyes

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