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Technoeconomic Assessment of the Impact of Window Improvements on the Heating and Cooling Energy Requirement and Greenhouse Gas Emissions of the Canadian Housing Stock

2013· article· en· W2024780270 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Energy Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsCarleton UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreenhouse gasEnvironmental scienceEnergy consumptionStock (firearms)Primary energyEfficient energy useHeating degree dayEnergy performanceNatural resource economicsEnvironmental engineeringRenewable energyEconomicsEngineering

Abstract

fetched live from OpenAlex

This study evaluates the economic feasibility as well as the effect of window modifications on the heating and cooling energy requirement of the Canadian housing stock based on detailed energy simulations conducted using the Canadian hybrid residential end-use energy model (CHREM) and green house gas emissions model (GEM). It is found that thermally improved windows can substantially reduce the energy consumption and greenhouse gas emissions from the Canadian residential sector. The magnitude of energy consumption and greenhouse gas (GHG) reductions depend largely on the size of the housing stock, existing window type characteristics, climate, and fuel mix used. Thus, there are variations from province to province. Similarly, economic feasibility depends on the magnitude of savings available as well as the price of energy in each province.

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.

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 score0.024
Threshold uncertainty score0.997

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 communication0.0000.000
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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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