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Record W4241252889 · doi:10.32920/ryerson.14649852

The impact of building envelope retrofit measures on postwar MURBS in Toronto

2021· preprint· en· W4241252889 on OpenAlexaffabout
Sara Damyar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan UniversitySciencetech (Canada)
Fundersnot available
KeywordsEnvelope (radar)Building envelopeEnergy consumptionConsumption (sociology)Ranking (information retrieval)Architectural engineeringEnergy (signal processing)Code (set theory)EngineeringBuilding codeEnergy performanceEfficient energy useComputer scienceCivil engineeringReliability engineeringTelecommunicationsElectrical engineeringGeographyMathematics

Abstract

fetched live from OpenAlex

Building envelope retrofits is one of the options available to reduce energy consumption of postwar MURBs in Toronto. This study evaluates the impact of building envelope retrofits that meet current standards on energy consumption of a Toronto postwar MURB; utilizing eQUEST energy simulation software. Further upgrades also take place to evaluate how the impact of building envelope retrofits on energy use can be increased and optimized for all assemblies of building envelope and airtightness. Moreover, the retrofit strategies are ranked based on cost and energy-saving effectiveness. The results of the analysis reveal that building envelope retrofit based on OBC-2012 standards can reduce the energy consumption by up to 44%. Furthermore, the optimal RSI values of all building envelope components were found to be equal or less than code requirements which outcomes significant energy savings. Lastly, the ranking of the strategies helps to identify the best option according to the priorities of a project.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.259
Teacher spread0.246 · 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 designObservational
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

Citations2
Published2021
Admission routes2
Has abstractyes

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