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Record W2053648348 · doi:10.1061/41099(367)15

Permeable Pavement Performance over 3 Years of Monitoring

2010· article· en· W2053648348 on OpenAlexaff
Elizabeth Fassman‐Beck, Sam Blackbourn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsAecom (Canada)
FundersUniversity of Auckland
KeywordsEnvironmental scienceSurface runoffPollutantHydrology (agriculture)StormWater qualityFirst flushAsphaltPervious concreteGeotechnical engineeringStormwaterGeology

Abstract

fetched live from OpenAlex

A 200 m2 permeable pavement test site was installed along Birkdale Road on Auckland's North Shore. Data from the permeable pavement section and an adjacent conventional asphalt section were collected concurrently in 2006 and 2008. Despite installation on an atypical high slope (6.5–6.8%), relatively impermeable subsoils, and active roadway, overall system performance was exceptional. For the 81 complete storms monitored for hydrology, peak flow, runoff timing and volume compared well to predevelopment conditions. A catchment designed on an LID-basis of controlling frequently occurring events would be well served using permeable pavement. Additional hydrologic control may be needed for design storms greater than 5-yr ARI. Water quality characterization for 4–17 storms (depending on pollutant type) was comparable to or better than typical end-of-pipe devices for TSS, PSD, total and recoverable Cu and Zn, and dissolved Cu and Zn. The permeable pavement discharge water quality had consistent event mean concentrations which were statistically lower than the conventional asphalt. Pollutant removal efficiencies are presented. A properly designed permeable pavement section would likely provide adequate treatment for an expanded source area. Permeable pavements should be given strong consideration as an LID at-source control.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.995

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations11
Published2010
Admission routes1
Has abstractyes

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