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Record W4249237242 · doi:10.26868/25222708.2019.210805

Application of A Housing Technology Assessment Simulation Platform in Regulation R&D

2020· article· en· W4249237242 on OpenAlexafffundabout
Rasoul Asaee, Alex Ferguson, Adam Wills

Bibliographic record

VenueBuilding Simulation Conference proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsNational Research Council CanadaNatural Resources Canada
FundersOffice of Energy Research and DevelopmentNatural Resources Canada
KeywordsArchetypeEnvelope (radar)Architectural engineeringBuilding envelopeCode (set theory)Computer scienceEnergy conservationZero-energy buildingMeasure (data warehouse)Passive houseCivil engineeringEnvironmental economicsEngineeringEfficient energy useTelecommunicationsEconomicsDatabaseElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents an optimization model that will be used for the impact analysis of new net zero energy ready model building codes, and to support the local authorities for the adoption of new building codes. New archetypes representing new single detached and double/row house for eight major housing markets across Canada were developed. A case study was conducted using the new archetypes to identify which building components were significant sources of heat loss when constructed to Canadian National Building Code minimums. Increasing envelope airtightness was then explored as an energy conservation measure.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.280
Teacher spread0.252 · 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

Citations4
Published2020
Admission routes3
Has abstractyes

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