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Record W7132360929

A systematic evaluation of indoor overheating interactions with outdoor heat conditions

2020· article· en· W7132360929 on OpenAlexvenueaboutno aff
Lili Ji, Abdelaziz Laouadi, Chang Shu, Abhishek Gaur, Michael Lacasse, Liangzhu Wang

Bibliographic record

VenueNPARC · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsOverheating (electricity)Extreme heatExtreme weatherHeat waveBuilding envelopeClimate change
DOInot available

Abstract

fetched live from OpenAlex

Overheating in buildings has been a major health concern for vulnerable occupants during extreme heatwaves. Even though indoor overheating is highly dependent on outdoor climate, it may also be affected by other factors including building envelope characteristics, HVAC operation and internal heat gains. This study is to investigate the synchronisation of indoor overheating with outdoor heatwave. Archetype residential buildings were created and used to conduct EnergyPlus simulations of 31 years climate data of three major Canadian cities. A heatwave evaluation method developed by National Research Council of Canada was used to define heat events and extreme summer weather years. The method uses the transient standard effective temperature (t-SET) to rank heat events in terms of duration, intensity and severity. The results show that in most building configurations, outdoor extreme heatwaves are synchronised with indoor extreme overheating events. Outdoor-based extreme summer weather years are therefore suitable to study indoor overheating risks.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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