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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.698

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)0.0000.000

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