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Record W4256096927 · doi:10.32920/ryerson.14643966

Long-term performance modelling of Etobicoke exfiltration system

2021· preprint· en· W4256096927 on OpenAlexaffabout
Haiyue Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnvironmental scienceStormwaterUrbanizationStormwater managementDetention basinLow-impact developmentWater cycleSubdivisionWater resource managementEnvironmental engineeringEnvironmental planningCivil engineeringSurface runoffEngineering

Abstract

fetched live from OpenAlex

Urbanization increases the stress on the hydrologic cycle. The Etobicoke exfiltration system (EES) was developed in 1993 to remediate the impact on the hydrologic cycle after urbanization. The purpose of this research is to model the Etobicoke exfiltration system (EES) and evaluate the stormwater management performance of EES. A comprehensive literature review was conducted on development of stormwater management and Low impact development (LID). The US EPA SWMM was selected to model the EES. Three modelling methods were investigated to simulate the performance of EES. The Orifice-Storage-Pump method was found to perform the best. EES was applied before an existing wet pond in a case study subdivision. The modelling results show that EES meets three criteria: reduce water quantity, impact water balance and improve water quality.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
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 science0.0000.001
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 head0.039
GPT teacher head0.213
Teacher spread0.174 · 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 designSimulation or modeling
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

Citations6
Published2021
Admission routes2
Has abstractyes

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