MétaCan
Menu
Back to cohort
Record W2048492071 · doi:10.1109/isbeia.2012.6422975

Comparative study on green roof mechanism in developed countries

2012· article· en· W2048492071 on OpenAlexaboutno aff
Zulhabri Ismail, Haziq Abd Aziz, Nasyairi Mat Nasir, Mohd Zafrullah Mohd Taib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsGreen roofGovernment (linguistics)BusinessRoofOrder (exchange)Construction industryGreen buildingEnvironmental planningArchitectural engineeringEconomic growthCivil engineeringGeographyEngineeringEconomicsFinanceConstruction engineering

Abstract

fetched live from OpenAlex

The building industry is known to have a great impact towards the environment in urban areas, green roof systems are seen as a way to mitigate its negative impacts. Developed countries such as Germany, Canada, Japan, Singapore and Hong Kong have widely adopted green roofs in urban areas in order to reduce the negative impact construction activities especially in the cities. Each one of these countries has their own methods of encouraging building owners to adopt green roofs. Developed countries such as Germany, Canada, Japan, Singapore and Hong Kong has taken their fare share in applying green roofs into their building industry. Thus, it is crucial for the Malaysian government to impose and instil the green roofs into our local building industry. This paper analyses the comparison of the green roof mechanism applied in Germany, Canada, Japan, Singapore and Hong Kong. The findings from this paper will contribute more information and better idea on how to implement and attract public sector in Malaysia to adopt green roofs.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

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)0.0020.002

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 head0.044
GPT teacher head0.281
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicUrban Heat Island MitigationFrench-language works237,207