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Record W3088198235 · doi:10.21608/jesaun.2019.115507

GREEN BUILDING INFORMATION MODELING (BIM) AND SUSTAINABLE DEVELOPMENT CASE STUDY: UNIVERSITY OF CANADA, THE NEW ADMINISTRATIVE CAPITAL, CAIRO, EGYPT

2019· article· en· W3088198235 on OpenAlexaboutno aff
Tamer Refaat, Ehab Hussien

Bibliographic record

VenueJES. Journal of Engineering Sciences/JES. Journal of engineering sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsBuilding information modelingSustainable developmentGreen buildingCapital (architecture)Environmental planningBusinessRegional scienceEnvironmental resource managementPolitical scienceGeographyArchitectural engineeringEngineeringOperations managementEconomicsArchaeology

Abstract

fetched live from OpenAlex

As a result of the political and economic developments taking place in Egypt to keep peace with other countries, the New Administrative Capital City is a beginning to achieve progress not only economically and socially but also environmentally. The project depends on meeting the user’s social, cultural and economic needs without harming the surrounding natural environment and by using renewable energy to achieve the principles of sustainability and green architecture.The aim of this research is to study the design and the effect of the New Administrative Capital City in economic and environmental, and how it achieves sustainability through urban design and detailed design. Also, discusses the different views of the project either positive or negative, with clear responses to negative views. Finally, it shows how could the New Administrative Capital City could be as a model to any designer want to create sustainable and ecofriendly city.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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