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Record W4256326057 · doi:10.32920/ryerson.14654754

Towards Sustainability : Prioritizing Retrofit Options For Toronto's Single-Family Homes

2021· preprint· en· W4256326057 on OpenAlexaffabout
Katarzyna M. Blaszak

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan UniversitySciencetech (Canada)University of Toronto
Fundersnot available
KeywordsEmbodied energyRanking (information retrieval)WeightingSustainabilityEstimatorArchetypeStock (firearms)Environmental economicsComputer scienceArchitectural engineeringEconometricsEngineeringMathematicsEconomicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigates a preliminary retrofit ranking framework for single-family homes on the basis of net environmental effect. Four archetype homes developed to represent Toronto's existing housing stock were modeled using HOT2000 to calculate the operational energy requirements. The embodied effects of selected retrofits were then calculated using the ATHENA Impact estimator and a list of environmental summary measures produced. A method of combining operational and embodied effects based on these eight summary measures was proposed and the functioning and sensitivities of the equation were explored. The method is preliminary and incorporates two factors, a weighting factor and building science factor, that require further research. Analysis of the simultated results allowed generalizations about energy performance and prioritized retrofit recommendations for archetypes. In most retrofit cases operational energy dominates, however, the ranking equation shows the potential for certain conditions in which the embodied effects determine the ranking of a 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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 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

Citations10
Published2021
Admission routes2
Has abstractyes

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