MétaCan
Menu
Back to cohort
Record W6921914470 · doi:10.11575/prism/40205

Deep Energy Retrofits in Housing for Low Income Household in BC and Manitoba: An Opportunity for Climate Mitigation and Social Equity

2022· other· en· W6921914470 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePRISM (University of Calgary) · 2022
Typeother
Languageen
FieldComputer Science
TopicEducational Robotics and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasBuilding envelopeElectrificationPublic housingEnergy consumptionElectricityClimate change mitigationLow-riseRenewable energy

Abstract

fetched live from OpenAlex

The low-income and vulnerable populations in Canada often live in social housing buildings with poor energy and environmental indoor performance. Many social housing buildings in Canada need major repairs and would also benefit from deep energy retrofits (DER) that could make them climate resilient, and safe for occupancy. The objective of this research was to investigate the GHG emissions reduction, as well as the energy and cost saving potential of different retrofit approaches in BC and Manitoba. I collected electricity and natural gas consumption data for 30 buildings in BC which were subsequently narrowed down to 6 building sites based on their location and type of retrofit. I also collected data for 2 buildings in Manitoba. One building received an interior insulating spray foam application, and the other, exterior spray foam. My study shows that different retrofit approaches executed in BC yielded 18% to 39% energy savings and 27% to 99% GHG emissions reduction as a result of the electrification of one or both end-use systems for space and water heating, as well as building envelope upgrade. Significant energy savings and GHG emissions reduction were also realized in the two Manitoba buildings where building envelope enhancements were executed including the installation of high efficiency heat recovery ventilation systems.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.913
Threshold uncertainty score0.575

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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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