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Record W4416573337 · doi:10.3390/architecture5040117

Lowering Energy and Material Consumption Through Modular Dwelling Design

2025· article· en· W4416573337 on OpenAlexaff
Avi Friedman

Bibliographic record

VenueArchitecture · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsMcGill University
Fundersnot available
KeywordsModular designModularity (biology)DeskEnergy consumptionKey (lock)SustainabilityConsumption (sociology)Efficient energy use

Abstract

fetched live from OpenAlex

This article investigates how the application of modular dimensions and disassembly methods can lower energy and material consumption in residential buildings. This study utilizes a non-reactive desk research and applied case study methods. The examination of precedent publications and studies encompassed the following subjects: The first stage defines modularity in housing and the concept of Design for Disassembly (DfD). The second stage of the research involves analyzing the prefabricated and modular Grow Home project that was designed and built by the author and his team, containing DfD principles, to reduce energy consumption and material waste. In the discussion section, the author highlights key barriers to modular homes in the construction industry. The findings demonstrate that by including several design strategies, such as the enhancement of modularity and DfD affordability, reduction in material waste, and increase in the overall sustainability of a given development.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
Teacher spread0.191 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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