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Record W2955898963 · doi:10.29173/mocs136

Investigation of Influencing Factors on Air Leakage of Canadian Dwellings

2019· article· en· W2955898963 on OpenAlexaffvenueabout
Maysoun Ismaiel, Yuxiang Chen

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLeakage (economics)Indoor air qualityEnvironmental scienceSizingEnergy consumptionThermalEngineeringEnvironmental engineeringMeteorologyGeography

Abstract

fetched live from OpenAlex

Air leakage is one of the main influencing factors in buildings’ thermal performance. The adverse effects of poor air leakage include higher energy costs, consumption in space heating and cooling, poor thermal comfort, corrosion, and the growth of molds due to air leakage induced condensation. The main objective of this study is to investigate the characteristics of air leakages of Canadian homes related to construction methods, age, size and climatic zones. The air leakage test results of 226,000 dwellings in three provinces of Canada were analyzed. Statistical analysis was utilized to compare the mean of air leakage with respect to different factors. Generally, the air leakage decreased by 40% in the period from 1960 until 2018, which has shown a remarkable effect of new construction techniques on air leakage. Investigations also indicated that the average air leakage rate of homes constructed by using the onsite technique is approximately 25% to 60% higher than those prefabricated in modular or panels, varying with respect to the workmanship and construction quality control. This study concluded that the prefabricated construction techniques could decrease the air leakage rate significantly, which will have a remarkable effect on buildings’ thermal performance as well as home’s heating and cooling costs. The findings contribute to estimating the effects of influencing factors on air leakage, also it is useful in performance simulations, HAVC sizing and energy management. And recommend the use of the prefabricated in modular or panel’s construction method to achieve better and acceptable air leakage performance.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.155
Teacher spread0.148 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2019
Admission routes3
Has abstractyes

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