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Record W4223595556 · doi:10.5772/geet.04

Productive Blue-Green Roofs for Stormwater Management

2022· article· en· W4223595556 on OpenAlexafffund
Kristiina Valter

Bibliographic record

VenueGreen Energy and Environmental Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreen roofStormwaterStormwater managementGreen infrastructureEnvironmental scienceEnvironmental planningRoofEnvironmental engineeringBusinessCivil engineeringEnvironmental resource managementEngineeringSurface runoffEcology

Abstract

fetched live from OpenAlex

Green roofs have been used around the world for centuries, and have been adapted to modern urban buildings. Many cities have now adopted a green roof bylaw in recognition of their environmental benefits, including stormwater management. Despite this requirement, if green roofs are poorly designed, they may quickly become ineffective or counterproductive. In this paper, features of green roofs that are important for sustained environmental benefit are highlighted with a focus on water demand and management. Blue roofs use specialized retention layers to delay stormwater run-off or retain it for evaporation. Blue and green roofs can be combined to grow productive, or edible crops and this use can have synergistic benefits. This paper describes case studies and testbeds of various combinations of green and blue roof sublayers with edible and non-edible plants. Design parameters are considered and monitoring and automation systems are described.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.166
Teacher spread0.162 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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