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Record W3209371080 · doi:10.32920/ryerson.14654460.v1

Impact of a compartmentalization and ventilation system retrofit strategy on energy use in high-rise residential buildings

2021· preprint· en· W3209371080 on OpenAlexaffabout
Mathew Carlsson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan UniversityUniversity of Ottawa
Fundersnot available
KeywordsEnclosureCompartmentalization (fire protection)Ventilation (architecture)Environmental scienceStack effectArchitectural engineeringGreenhouse gasSuiteHVACEngineeringAir conditioningMeteorologyMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

A compartmentalization and in-suite ventilation system (ISVS) retrofit strategy was investigated for an existing high-rise residential building in Vancouver. Computer simulation using EnergyPlus™ was used to examine the impact of the proposed retrofit on heating energy and GHG emissions for the building’s original 1983 condition, and its current condition which incorporates a 2012 enclosure retrofit. Results show annual heating energy decreased by 51% and overall GHG emissions decreased by 29% for the proposed retrofit applied to the building in its current condition. When applied to the building in its original condition, heating energy decreased by 49% and overall GHG emissions decreased by 21%. The main benefit of the proposed retrofit, however, is improved effectiveness of the mechanical ventilation system. Because building enclosure air-tightness improvements can negatively impact air distribution in buildings with pressurized corridor ventilation systems, the proposed retrofit should be applied in combination with, or before, an enclosure retrofit.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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