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Record W4247371460 · doi:10.32920/ryerson.14645811.v1

Determining the effect of external shading strategies on energy and daylight of fully glazed office buildings in Toronto

2021· preprint· en· W4247371460 on OpenAlexaffabout
Maryam Sadat Morakabian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDaylightOverheating (electricity)ShadingArchitectural engineeringBuilding envelopeEnergy consumptionEnvironmental scienceEngineeringComputer scienceMeteorologyGeographyOpticsElectrical engineeringPhysicsThermalComputer graphics (images)

Abstract

fetched live from OpenAlex

With regard to overheating and heat lost potential, transparent parts of the building envelope have a significant impact on building energy consumption. The project investigates the impact of fully glazed façades on energy intensity and daylight quality of office buildings in Toronto. Also it explores the potential of various external shading strategies in minimizing energy consumption and enhancing daylight quality. Different types of external shadings including overhangs, vertical fins and diagrid screen were investigated in terms of heating, cooling and lighting energy use and daylight performance. The diagrid screen, in comparison to conventional shading strategies, demonstrates significantly better impact on cooling load reduction and improving daylight quality in interior spaces. This study helped to evaluate the strengths and weaknesses of each shading system and provides guidelines for architects in the process of designing façades of glazed office buildings to widen the options for aesthetically pleasing, high performance façades.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.631
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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 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
Published2021
Admission routes2
Has abstractyes

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