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Record W4387375742 · doi:10.1139/cjce-2023-0222

Unified life-cycle cost–benefit analysis framework and critical review for sustainable retrofit of Canada's existing buildings using mass timber

2023· article· en· W4387375742 on OpenAlexafffundvenueabout
Daniele Malomo, Yazhou Xie, Ghasan Doudak

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of OttawaMcGill University
FundersCanadian Forest Service
KeywordsRetrofittingLife-cycle cost analysisArchitectural engineeringContext (archaeology)EngineeringEnvironmental resource managementEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

This paper investigates the possibilities and challenges of using mass timber as a sustainable alternative for retrofitting existing buildings in Canada. To create the knowledge foundation on which to devise a holistic framework tailored to the specific characteristics of Canada's built environment, a detailed analysis of the types, geographical distribution, structural, and energy features of the local building stock is first presented. Then, previous strategies for enhancing and upgrading existing buildings with engineered timber are reviewed, classified, and evaluated. Finally, to explore economic and environmental implications, a detailed assessment of available life-cycle cost–benefit analysis approaches is conducted alongside their adaptation to Canada's building context. The findings of this paper can inform policymakers, builders, and designers in developing more sustainable building retrofit practices, design, and regulations, in line with Canada's efforts toward net zero emissions.

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)0.020
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.474
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.010
Science and technology studies0.0020.003
Scholarly communication0.0060.003
Open science0.0040.001
Research integrity0.0030.003
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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 designTheoretical or conceptual
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

Citations13
Published2023
Admission routes4
Has abstractyes

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