Climate-Resilient Buildings and Core Public Infrastructure: summary of state-of-practice and knowledge gaps on climate change adaptation of buildings and core public infrastructure
Bibliographic record
Abstract
Canada’s buildings, bridges, roads, rail transit, potable water systems and wastewater systems provide shelter and safe drinking water, enable transport of people and goods, and collect and treat wastewater. They provide basic and core services for Canadians and are critical to Canada’s economy and the nation’s quality of life. These buildings and core public infrastructure (B&CPI) are subject to time-varying and uncertain effects due to climate change, as well as more frequent extreme weather events, such as flooding. These climate changeinduced pressures could potentially reduce the safety, serviceability, functionality, and service life of Canada’s B&CPI. In addition, Canadian B&CPI are aging and deteriorating due to different degradation mechanisms, as well as inadequate inspection and maintenance practices – issues potentially made worse by the effects of climate change. The Climate-Resilient Buildings and Core Public Infrastructure (CRBCPI) initiative was established as a research and development partnership between the National Research Council of Canada (NRC) and Infrastructure Canada to develop new and revised codes, standards, specifications, guidelines and decision support tools to ensure that Canada’s new and existing B&CPI are more resilient to the effects of climate change and extreme weather events. Funded by Infrastructure Canada and managed by NRC, the effort brings together experts from across Canada. Initiative priority areas include buildings, bridges, roads, potable water, storm water, and wastewater systems, and rail transit guideways. The initiative addresses resilience to climate change impacts on temperature, precipitation, wind and extreme weather events, including flooding, wildfires and extreme wind, which can affect these types of B&CPI across the country.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".