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Record W161976239 · doi:10.14796/jwmm.r208-08

Evaluation of Stormwater Retrofit Options for Mimico Creek Watershed

2002· article· en· W161976239 on OpenAlexaffvenue
James Li, Kosta Kyriopoulous

Bibliographic record

VenueJournal of Water Management Modeling · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStormwaterWatershedSurface runoffEnvironmental scienceStormWater qualityHydrology (agriculture)Water resource managementPollutionNonpoint source pollutionGeographyEngineeringMeteorologyComputer science

Abstract

fetched live from OpenAlex

One of the major water pollution sources in the fully urbanized Mimico Creek watershed is storm runoff.In order to control stormwater quality, retrofit stormwater management practices (RSWMPs) should be implemented.This chapter documents a study which focuses on the evaluation of appropriate RSWMPs for the Mimi co Creek watershed.These practices are selected based upon physical site constraints, cost-effectiveness, and the potential to be incorporated into municipal capital works and maintenance programs.Using a derived probabilistic rainfall-runoff model and a treatment train efficiency model, the cumulative reduction of runoff volume and solids loading of a series of appropriate RSWMPs are determined to be 7% and 18% respectively.In the Mimico Creek watershed, the descending order of cost-effectiveness is: (i) downspout disconnection; (ii) water quality ponds; (iii) stormwater exfiltration systems; and (iv) oil/grit separators.Thus, the sequence ofRSWMP implementation should follow the descending order of cost-effectiveness.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.990
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.243
Teacher spread0.195 · 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

Citations0
Published2002
Admission routes2
Has abstractyes

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