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Record W140406337 · doi:10.7275/4925868

A Comparison of American, Canadian, and European Home Energy Performance in Heating Dominated – Moist Climates Based on Building Codes

2023· article· en· W140406337 on OpenAlexaboutno aff
Stephanie Berkland

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2023
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

This research compares the energy performance of a code-built residential building within the moist climate zone classification in Canada, Europe, and the Northeastern United States. The primary objectives are to reveal how specific differences in code requirements in similar climates influence a building’s energy profile, offer a means to quantify and evaluate the extent of energy savings as a result of each requirement, and provide a comparison of each location’s building culture and how this affects the standards in place.\nUsing the building energy simulation tool, DesignBuilder EnergyPlus Simulation, a model single-family home was created and input energy code requirements for each location. An evaluation of each location’s building culture is examined through such factors as the training of building professionals, commonly used materials and products, energy reduction goals, and cultural attitudes.\nThe results of this study point to the need for more advanced building practices, stricter code mandates, and higher performing products based on energy savings achieved from buildings built to different standards in equivalent climate zones. This has the potential to drive the development and use of better performing building materials and assemblies in the future.

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 score0.022
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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