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Record W4405107717 · doi:10.1080/19401493.2024.2435908

Towards codification of thermal resilience upgrades in midrise residential buildings: a Canadian archetype energy model approach

2024· article· en· W4405107717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Building Performance Simulation · 2024
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsArchetypeArchitectural engineeringResilience (materials science)Energy (signal processing)Key (lock)EngineeringCivil engineeringEnvironmental scienceComputer scienceComputer securityPhysicsArt

Abstract

fetched live from OpenAlex

Extreme weather events are predicted to continue increasing in intensity and frequency, raising a serious concern about the thermal resilience and adaption of buildings to these events. A single residential archetype building will be chosen as the case study and evaluated in three locations across Canada (Vancouver, Ottawa, Charlottetown) using three historical extreme weather events. Two upgrade scenarios will be considered, a code-compliant (NECB 2020) and a high-performance building option, to compare thermal performance through hot/cold weather outages. By using a variety of outage events in cold-climate zones for an archetype building, this paper’s methodology offers a useful framework for building practitioners to assess the effects of building upgrades on performance metrics and improve building resilience. This research could eventually contribute to the development of codes by providing upgrade packages to create infrastructure that both mitigates and adapts to future climate change extreme weather.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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