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Record W7127994155 · doi:10.22260/crc-csce-2025/0049

Transforming Canada's Housing Stock: A Comprehensive Categorization of Sustainable Retrofit Strategies for Residential Building

2025· article· W7127994155 on OpenAlexaboutno aff
Basil Khan, Mehdi Ghobadi, Kartik Patel, Rajeev Ruparathna

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsCategorizationSustainabilityWork (physics)Identification (biology)Sustainable development

Abstract

fetched live from OpenAlex

Single-family detached and attached homes constitute a significant portion of Canada's housing stock, many of which were constructed during the pre-sustainability era.Given their substantial energy demand, retrofitting these buildings is essential for meeting Canada's climate action goals.With a wide range of retrofit options available to reduce energy use, improve comfort, and enhance aesthetics, it is crucial to identify solutions that not only improve environmental performance but also generate positive economic and social impacts.Few resources currently classify residential building retrofits based on their primary focus.This study investigates retrofit strategies for Canada's single-family homes by evaluating their impact on building performance and categorizing innovative hybrid approaches that balance energy efficiency, cost-effectiveness, aesthetic appeal, and structural resilience.Our findings indicate that implementing hybrid retrofit strategies in Canada's residential sector requires an integrated approach addressing technical, financial, and regulatory challenges.Moreover, a structured classification of retrofit types, combined with robust stakeholder engagement beyond mere financial incentives, is essential to achieving significant improvements in energy efficiency, sustainability, and resilience.Ultimately, this research provides a framework for transforming existing residential buildings into energy-efficient, visually appealing, and regulation-compliant homes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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
Published2025
Admission routes1
Has abstractyes

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