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Record W2934828981 · doi:10.3390/w11040723

The Effectiveness of Exfiltration Technology to Support Sponge City Objectives

2019· article· en· W2934828981 on OpenAlexaffabout
Edward A. McBean, Guohe Huang, Aili Yang, Huiyan Cheng, Yicheng Wu, Zheng Liu, Zhineng Dai, Haiyan Fu, M. A. Bhatti

Bibliographic record

VenueWater · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of ReginaUniversity of Guelph
Fundersnot available
KeywordsStormwaterSurface runoffEnvironmental scienceGroundwater rechargeWater resource managementHydrology (agriculture)Flooding (psychology)GroundwaterWater qualityFlash floodLow-impact developmentEnvironmental planningEnvironmental engineeringFlood mythGeographyStormwater managementAquiferGeology

Abstract

fetched live from OpenAlex

Urban stormwater management is essential to improve the management of floodwaters in municipalities in urban areas. However, relying on sponge city options for site planning in an attempt to decrease the impacts of flooding is challenging due to the magnitude of flooding in urban China. The merits of exfiltration technology being used in Canada are described as having significant potential; this technology encourages passage from the stormwater pipe down to a second, lower pipe, to facilitate exfiltration to the vadose zone and, ultimately, to replenish groundwater. For example, for a small urban catchment, stormwater runoff from a 2-h long, 5-yearly storm, is demonstrated as being able to exfiltrate approximately 53% of the stormwater. Overall, the potential exists to exfiltrate stormwater from the lower pipe and it is estimated that 71% of the water entering the storm sewer is exfiltrated to the vadose zone, for a small catchment. The exfiltration pipe technology increases groundwater recharge which provides an opportunity to help manage subsidence in China. However, attention must be paid to the quality of the infiltrating water since, as true for any sponge city initiative, poor quality infiltrating water may deteriorate the quality of the groundwater.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.998

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)0.0010.002

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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 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

Citations12
Published2019
Admission routes2
Has abstractyes

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