A meta-analysis of the schematic design process of deep retrofit projects
Bibliographic record
Abstract
Deep retrofits of the existing building stock will be necessary to meet global emissions reductions targets. One building archetype, low-rise MURBs have been neglected in terms of research and funding for deep retrofits. A meta-analysis was conducted that compares and contrasts the schematic design approach taken for six such buildings in British Columbia which are scheduled to undergo deep retrofits with the goal of reducing GHG emissions by 80%. The analysis showed that design teams had converged toward common solutions for each building while achieving the GHG reduction target. The recommended measures include electrification of space and domestic hot water heating, adding insulation through overcladding, air sealing, ventilators for each unit, and double pane windows. A life cycle cost analysis showed that the economic viability of deep retrofits were dependent on energy price forecasts, capital cost reductions through market forces and transformation, or incentives cover the non-monetizable co-benefits of deep retrofits such as improved resiliency to climate-change or reducing overheating and air quality risks. The meta-analysis can help to streamline the early-stage and schematic design process for such buildings, which is critical to increasing the retrofit rate. This process could be replicated for other building types and construction archetypes.
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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 | 0.049 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".