Buildings design climate datasets for Canadian cities incorporating potential effects of climate change, urban heat island, and nature-based solutions
Bibliographic record
Abstract
Canada has experienced a warming trend since the industrial revolution, surpassing the global average by a factor of two, with its northern regions experiencing an even more pronounced threefold increase in temperature. This significant and consistent shift in climate has led to extensive alterations in climate patterns nationwide, intensifying the frequency, severity, and duration of climate-related natural disasters such as floods, wildfires, and heatwaves. Canadians typically spend 90% of their time indoors in various residential, commercial, and institutional structures. Thus, enhancing the resilience of these buildings and preparing them for anticipated climate changes is crucial for maintaining the well-being of the Canadian population in the face of a changing climate. To design buildings and to assess building performance against the varying outdoor climate over their lifespan, the National Research Council Canada (NRC) has developed novel methods to prepare future projected climate datasets incorporating the effects of climate change, as well as urban heat islands. Furthermore, potential cooling which can be achieved by implementing city-scale nature-based solutions such as increased greenery and albedo, are also simulated and integrated into the climate datasets. In this paper, the approach and key aspects of aforementioned building simulation climate data will be provided. The NRC are currently using these files to incorporate climate resilience and nature-based solutions into building designs under the Government of Canada's Climate Resilient Built Environment Initiative.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".