Roadmap to enable climate resilient adaptation retrofits
Bibliographic record
Abstract
Many structures across Canada were designed and constructed in accordance with earlier versions of the building code, based on the assumption that climatic loads would remain stationary over the service life of the building. However, due to Greenhouse Gas (GHG) emissions, Canada’s climate has become increasingly variable, and this trend is expected to continue. The frequency and intensity of natural hazard events are rising and regions previously unaffected by certain climate events may now experience them for the first time. In addition, many Canadian jurisdictions are likely to face multiple competing or compounding climate hazards, including concurrent events. These evolving conditions are already placing pressure on building owners, who are now required to undertake more frequent, unanticipated, repairs and invest in climate resilient upgrades, a demand that is projected to grow as climate impacts intensify. As more occupants and building owners pursue adaptation strategies to enhance the climate resiliency of their properties, there becomes a growing need for clear, evidence-based guidance. Such guidance must ensure that the selected measures do not result in unintended consequences or maladaptation, particularly in the context of competing hazards. To address this need, a roadmap is proposed, which outlines the key steps required between hazard identification to adaptation implementation. This roadmap also serves as the organizational framework for this report, which reviews the information available to individuals and professionals seeking to design for a changing climate. This report focuses on the risks and vulnerabilities of buildings and their occupants to hazards such as wildfire, wind, extreme heat, flooding, and multi-hazard scenarios. The report also emphasizes individual building-level retrofits, including upgrades to the building envelope, mechanical, electrical, and plumbing systems, as well as improvements to the immediate surrounding property, rather than strategies that are to be implemented on a broader community scale. The insights and recommendations in this report can inform the future development of best practice guidelines, enabling individuals and professionals to navigate the full adaptation process, from identifying relevant hazards to implementing tailored retrofit solutions based on the specific needs of their building and location.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.021 |
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".