Bibliographic record
Abstract
In order to have an immediate impact on building energy-related emissions, existing buildings need to be addressed. To inform policy and technical decisions, detailed mathematical models are required which can explore numerous building retrofit solutions and their energy and emissions performance. This paper describes the adaptation of a hybrid statistical and engineering-based model for residential building stock in Canada to analyze community-scale energy retrofits. The modelling domain includes both envelope and mechanical retrofits, as well as district renewable energy systems. This model is then applied to a case study of converting a community of fifty 1980’s vintage single-detached homes to achieve net-zero energy. The case study demonstrated that deep envelope retrofits and fuel switching from natural gas to electric heat pump systems reduce community energy demand by 69%. Saturating available roof area with photovoltaics was able to achieve net-zero balance. By considering net-zero at the community-scale, individual buildings that did not achieve net-zero were offset by net-exporting neighbours. Annual community emissions of the retrofit reduced emissions by 95%. The analysis also highlights the significant impact on electrical infrastructure due to solar generation and energy demand mismatch.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.871 | 0.867 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".