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Record W2885080090 · doi:10.1061/9780784481783.023

Canadian Low Impact Development Retrofit Approaches: A 21 <sup>st</sup> -Century Stormwater Management Paradigm

2018· article· en· W2885080090 on OpenAlexaffabout
William R. Trenouth, William Kyle Vander Linden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsCredit Valley Hospital
Fundersnot available
KeywordsStormwaterLow-impact developmentIncentiveEnvironmental planningBusinessIncentive programLegislationParadigm shiftUrbanizationScale (ratio)Stormwater managementEnvironmental scienceEconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Southern Ontario and, more generally, urban areas throughout Canada, have seen the implementation and successful operation of a number of pilot scale low impact development (LID) projects. Despite demonstrated success and a proclivity to outperform design expectations, adoption of LID practices—particularly on predeveloped private property—has not occurred. This is true even in the face of ongoing urban flooding and water quality impairment issues. While several factors have been identified as barriers hindering broad scale LID implementation, market research conducted in the Greater Toronto Area (GTA) indicates that the high up-front costs and long or non-existent payback period are the largest barriers inhibiting LID adoption. To address this, a mix of innovative policy and market-based tools, incentives, and approaches are needed. The Drainage Act, a 183 year old piece of legislation unique to the Province of Ontario, has been intensely scrutinized and found suitable as a vehicle to facilitate cost-optimized, community-based retrofit implementation. However, to incentivize the use of the Drainage Act process, a paradigm shift in how municipalities approach stormwater management is required to address the up-front costs and long payback periods associated with LID retrofits. Recognizing the offsite benefits provided by private property adoption and rewarding appropriately will promote an approach to managing stormwater that is not only cost effective and affordable, but linked and integrated at a watershed scale in a way that meets water quality targets in the face of increasing urbanization and climate change.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.192
Teacher spread0.184 · 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 designNot applicable
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

Citations2
Published2018
Admission routes2
Has abstractyes

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