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Record W2974644553

An Evaluation of Low Impact Development and Residential Basement Flood Risk in the Greater Toronto Area

2019· dissertation· en· W2974644553 on OpenAlexaboutno aff
Sandra Vrban

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythGeographyEnvironmental planningBasementEnvironmental scienceWater resource managementArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Low impact development (LID) is a modern engineering design strategy that functions to mitigate the negative impacts of increased stormwater runoff and pollution by managing stormwater quality and quantity. It was hypothesized that the incorporation of LID may exacerbate hydrologic conditions and contribute to residential basement flood risk. The hydrologic model PCSWMM was used to assess the influence of LID on net infiltration depth and groundwater elevations using continuous simulation for a one-year period for three case-study sites located in the Greater Toronto Area. The inclusion of LID did influence site hydrologic parameters, though these results are inconclusive. However, it can be inferred that the increased risk of basement flooding due to exacerbation of hydrologic parameters would depend on the extensiveness of LID implementation. A novel methodology for the assessment of LID influence on residential basement flood risk was established using hydrologic modeling and probabilistic assessment techniques.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.014
GPT teacher head0.250
Teacher spread0.235 · 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
Published2019
Admission routes1
Has abstractyes

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