MétaCan
Menu
Back to cohort
Record W4237290333 · doi:10.32920/ryerson.14651970

A Comparison of Building Envelope Performance Levels Between Ontario, Denmark, Germany and the Passive House Standard, in the Low-Rise, Residential Context

2021· preprint· en· W4237290333 on OpenAlexaboutno aff
Blaine Attwood

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
FundersBundesinstitut für Bau- Stadt- und RaumforschungDanish Enterprise and Construction AuthorityU.S. Department of State
KeywordsPassive houseContext (archaeology)Building envelopeSingle-family detached homeArchitectural engineeringLow-riseEnvelope (radar)Efficient energy useEnvironmental scienceGeographyAgricultural economicsCivil engineeringEngineeringMeteorologyTelecommunicationsArchaeologyEconomics

Abstract

fetched live from OpenAlex

This research compared and analyzed where the Ontario Building Code rates in the low-rise, residential sector in terms of its: in comparison to Denmark, Germany and the Passive House Standard. This was analyzed to see how Ontario compared against other world renowned energy efficient regulations and where or if there was room for improvement. For this, HOT2000 and THERM were utilized on all four of the reference standards, where both of these programs were managed in a way to compare the results of ‘typical’ building envelopes and the current regulation from each of the standards. These results were then able to provide a whole home’s heating and air conditioning energy use in the Greater Toronto Area climate. Overall, the results illustrated Ontario homes consume the most energy for both typically constructed homes and homes utilizing the minimum requirements. In addition to this, Ontario also had the least performing building envelope connection details. In total, the Passive House performed at the highest level followed by Germany, Denmark and then Ontario.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.321
Teacher spread0.280 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicFacilities and Workplace ManagementFrench-language works237,207