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Record W4312947284 · doi:10.1063/5.0102967

Energy-efficient educational building design based on green building concept and its stability analysis

2022· article· en· W4312947284 on OpenAlexaff
K. S. Anandh, Mahamoud Irshad, Aravind Murali, M. G. Soundarya Priya

Bibliographic record

VenueAIP conference proceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsCarleton University
Fundersnot available
KeywordsArchitectural engineeringGreen buildingStability (learning theory)Computer scienceEfficient energy useEnergy (signal processing)EngineeringElectrical engineeringMathematicsMachine learning

Abstract

fetched live from OpenAlex

Rapid urbanization and globalization significantly influence society, necessitating the use of non-renewable natural resources to create electricity. The electricity demand has risen substantially in recent years, owing to the sophisticated lifestyles of today’s rapidly expanding population. This demand can be gradually reduced by harnessing natural resources such as sunlight and wind. In this study, a G+7 educational building is designed using green building construction methods, and its stability is tested under various load combinations. Appropriate shading and ventilation designs are incorporated to minimize the energy consumed; as a result, the designed energy-efficient model is compared to a similar conventional building using an energy simulation tool to determine the quantum of electricity conserved. The investigation found that modifications in shading and ventilation have a more significant proportional influence on controlling artificial energy usage in buildings.

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)0.001
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: none
Teacher disagreement score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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