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Record W3154080337 · doi:10.1061/jswbay.0000951

Seeking More Cost-Efficient Design Criteria for Infiltration Trenches

2021· article· en· W3154080337 on OpenAlexaffabout
Elizabeth Rowe, Yiping Guo, Zhong Li

Bibliographic record

VenueJournal of Sustainable Water in the Built Environment · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPercentileStormSurface runoffInfiltration (HVAC)StormwaterEnvironmental scienceComputer scienceStatisticsMathematicsMeteorologyGeography

Abstract

fetched live from OpenAlex

For many jurisdictions, the current design criteria for low-impact development practices (LIDs) including infiltration trenches require LIDs to provide enough storage capacity to store catchment runoff from the location’s 90th-percentile storm. The 90th-percentile storm used in Ontario, Canada, has a depth of approximately 25 mm. This study examines the performances and costs of infiltration trenches built in Ontario but sized to accommodate alternative storm depths ranging from 5 to 50 mm. Analytical equations are used to determine the runoff reduction ratios of infiltration trenches, and a cost estimation tool specifically developed for LIDs is used to estimate their overall costs. Results indicate that the current 90th-percentile storm criterion is probably too high and not cost efficient. An evidence-based methodology for selecting more appropriate design criteria is proposed. Using this methodology, it was found that the economically more efficient design criterion for Ontario averages about 20–22 mm for different design cases. Significant savings can be realized if a lower design criterion is implemented. The proposed methodology is therefore recommended for jurisdictions seeking more cost-efficient design criteria.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.038
GPT teacher head0.268
Teacher spread0.230 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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