Balancing Trade-offs between Deep Energy Retrofits and Heritage Conservation: A Methodology and Case Study
Bibliographic record
Abstract
Drastic reductions in energy consumption within existing buildings are required to achieve climate change mitigation targets. However, a portion of existing buildings have important historic values that need to be conserved. The goal of this paper is to present a methodology and decision-framework for deep energy retrofit analyses that balances trade-offs between conservation and sustainability. This methodology includes historic recording, documentation, a detailed energy model, and calibration to monthly data. An historic house in Ottawa, Canada is studied to demonstrate the use of the methodology. The energy retrofit analysis suggests 67% energy savings are achievable by increasing envelope thermal resistance to 4.1 m2-K/W, reducing air infiltration by 70% to 4.2 ACH at 50 Pa through air sealing and an air-vapour barrier, rehabilitating windows to be triple-pane low-E assemblies, using an air-source heat pump to supplement the existing gas boiler, daylight sensors and controls, and solar PV panels.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".