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Assessment of natural ventilation using a whole-building simulation model: a case study of a landmark building

2019· article· en· W2981938576 on OpenAlexaffabout
Marc-Antoine Jean, Rohit Upadhyay, Chris Flood, Rodrigo Mora

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsBritish Columbia Institute of TechnologyPageau Morel and Associates (Canada)
Fundersnot available
KeywordsNatural ventilationLandmarkVentilation (architecture)Architectural engineeringBuilding scienceBuilding modelStratification (seeds)Natural (archaeology)Environmental scienceBuilding designBuilding energy simulationComputational fluid dynamicsComputer scienceCivil engineeringMeteorologySimulationEngineeringGeologyGeographyAerospace engineeringEnergy performanceArtificial intelligenceEfficient energy use

Abstract

fetched live from OpenAlex

Whole-building simulation and field measurements were conducted at two levels: at the whole building level and at the local-space level, at a landmark building in Vancouver, Canada. The analysis shows that calibrated whole-building simulations are accurate enough and useful for the assessment of natural ventilation. For large complex spaces, the analysis needs to be coupled with computational fluid dynamics to predict thermal stratification. In the case study building, such coupling was not critical, because changes in the natural ventilation original design intent caused a large atrium to be decoupled from the rest of the building, which was detrimental to the effectiveness of the natural cooling of the building in the summer.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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 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
Published2019
Admission routes2
Has abstractyes

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