MétaCan
Menu
Back to cohort
Record W2026983324 · doi:10.3992/1943-4618-9.2.124

DETERMINING THE EFFECT OF BUILDING GEOMETRY ON ENERGY USE PATTERNS OF OFFICE BUILDINGS IN TORONTO

2014· article· en· W2026983324 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 Green Building · 2014
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsContext (archaeology)Energy consumptionArchitectural engineeringGeometryArchetypeEnergy (signal processing)Measure (data warehouse)Building designArchitectural designEngineeringCivil engineeringComputer scienceMathematicsGeographyStatisticsDatabaseArchitecture

Abstract

fetched live from OpenAlex

ABSTRACT The project investigated the potential of building geometry to minimize energy consumption in office buildings. Five distinct geometries were modeled as mid-size office occupancies in the context of Toronto, Ontario, and examined with varied design parameters: window to wall ratio (WWR) and external static shading devices. IES VE software was used to predict the annual energy consumption of the five archetypes for 40 permutations. The outcome of this research showed that the variation of the total energy use from one shape to another was relatively small. WWR appeared to have a stronger impact on the energy pattern of a building than its shape. Overall, the energy performance of the archetypes were observed to conform to their individual building aspect ratios. The findings are thus expected to provide useful guidelines for architects on utilizing building geometry as an energy saving measure in the design of office buildings.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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