A meta-analysis of the schematic design process of deep retrofit projects
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.
Bibliographic record
Abstract
• Meta-analysis performed on the deep retrofit proposals for six social housing MURBs. • A convergence of design solutions for deep retrofits of low-rise MURBs. • Deep retrofits of MURBs in British Columbia can reduce GHG emissions by 80% • Proposed deep retrofits reduce TEUI by 45–81%, TEDI by 57–81% • Market transformation is key to improving the financial viability of deep retrofits. 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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it