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Record W3015882730 · doi:10.1016/j.wen.2020.03.003

Integration of green and gray infrastructures for sponge city: Water and energy nexus

2020· article· en· W3015882730 on OpenAlexaff
Yongjun Sun, Li Deng, Shu-Yuan Pan, Pen‐Chi Chiang, Shailesh S. Sable, Kinjal J. Shah

Bibliographic record

VenueWater-Energy Nexus · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
FundersGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsRainwater harvestingGray (unit)Green infrastructureEconomic shortageNexus (standard)Civil engineeringEnvironmental resource managementBusinessEnvironmental planningEngineeringEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

In the past few decades, urban flooding and water shortages caused by the rapid expansion of cities and the destruction of construction ecology have been harshly lost. The current ecological rainwater management system is based on the traditional gray infrastructure and cannot effectively solve the water problems of different scales. Sponge city, as an advanced rainwater management technology, plays a vital role in urban transformation and new construction. While building a sponge city, the gray infrastructure will be integrated to form a gray-green infrastructure integration, and the relationship between water and energy in the sponge city will be coordinated. This paper proposes the problems encountered in the transformation of the gray infrastructure of the sponge city to the green infrastructure and the measures to be taken. The integrated indicator system is used to comprehensively evaluate the integration of the gray-green facilities.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.198
Teacher spread0.182 · 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 designTheoretical or conceptual
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

Citations92
Published2020
Admission routes1
Has abstractyes

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