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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 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)0.001
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 score0.792
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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