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Record W4293108473 · doi:10.5383/juspn.16.02.003

Design and Study of a Digital Energy Building: Case of Morocco

2022· article· en· W4293108473 on OpenAlexvenueno aff
Abdelali Agouzoul, Mohamed Tabaa, Badr Chegari, Meryem Mhenni, Emmanuel Simeu, Abbas Dandache, Karim Alami

Bibliographic record

VenueJournal of Ubiquitous Systems and Pervasive Networks · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsIndustrial RevolutionUrbanizationEnergy consumptionWork (physics)Consumption (sociology)Architectural engineeringGlobalizationQuality (philosophy)Digital RevolutionBusinessProduction (economics)Energy (signal processing)Environmental economicsOperations managementComputer scienceEconomic growthEngineeringEconomicsPolitical scienceMechanical engineeringTelecommunicationsSociologyMarket economy

Abstract

fetched live from OpenAlex

In the industrial revolution that followed, the aim of the industry is not only to improve and meet its urgent needs, but also to improve the standard of living of society and make life easier for consumers. Therefore, economic growth must always be closely linked to the industrial revolution. The medical industry, energy conservation and, in particular, production technologies will be transformed through new value chain models. Globalization, urbanization, vital changes and the energy transformation are all shifting forces that assess the dynamics of technology to better identify solutions in the moving world. In recent years, successive revolutions have made remarkable contributions to a person's quality of life, safety, industrial economy, comfort and health. This work aims to improve the energy consumption of the building. To achieve this goal, digital twins were created to faithfully reflect the behavior and characteristics of future or current buildings. To replicate copies of future or existing buildings, we chose to use Autodesk REVIT solution to meet some limitations. These have a great influence on the energy behavior of the building.

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 categoriesnone
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 score0.132
Threshold uncertainty score0.327

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 head0.012
GPT teacher head0.210
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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