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Record W258649023 · doi:10.2166/wqrj.2009.006

Street Sweeping as a Method of Source Control for Urban Stormwater Pollution

2009· article· en· W258649023 on OpenAlexafffundabout
Quintin Rochfort, Kirsten Exall, Jonathan P'ng, Vicky Shi, Vesna Stevanovic-Briatico, Sandra Kok, Jiří Maršálek

Bibliographic record

VenueWater Quality Research Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsToronto Public HealthEnvironment and Climate Change Canada
FundersGovernment of Canada
KeywordsStormwaterEnvironmental scienceSurface runoffSedimentPollutionHydrology (agriculture)Environmental engineeringSedimentationFirst flushUrban runoffNonpoint source pollutionGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract The effectiveness of street sweeping as a source control measure for stormwater pollution was tested at a site in Toronto, using three types of sweepers employed by the City. A paired-plot experimental design was employed along an arterial road with a traffic volume of 26,000 vehicles/day. Typically, after several days of dry weather, one roadway plot was swept by the available sweeper (treated) and the following plot was left unswept (control). After sweeping, sediment on the roadway was sampled on both plots; wet samples were collected by washing off one half of each plot, and dry samples were collected by vacuum cleaning the remaining halves of both plots. Differences between swept and unswept plots were assessed by comparing: (a) conventional sediment quality parameters, total residue mass, and particle sizes for dry sediment samples, and (b) toxicity, conventional water quality parameters, and particle sizes in wet samples. Results were highly variable and contained large uncertainties. The greatest environmental benefits of sweeping were the reduction of the total mass of sediment on road surfaces and a reduction in some dissolved metals in the runoff (e.g., Cr and Zn).

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.094
GPT teacher head0.410
Teacher spread0.316 · 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.

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

Citations19
Published2009
Admission routes3
Has abstractyes

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