Building reference year climate datasets for 564 reference locations in Canada
Bibliographic record
Abstract
Climate change in the future will continue to bring about unprecedented climate and climate extremes, and buildings and infrastructure will be exposed to them. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. Reference years are one year (or a few years) prepared from the climate time series to capture aspects of interest from the long-term climate datasets. This database provides access to the following building simulation reference year files for 564 locations in Canada. 1. Typical Meteorological Year data for building energy applications are prepared using Sandia method (Hall et al. 1978; NREL 2008) by concatenating twelve typical meteorological months selected based on Finkelstein‐Schafer (FS) statistics. 2. Temperature reference years: Typical Downscaled Year, Extreme Cold Year, and Extreme Warm Year data are prepared following Nik (2016; 2017) by concatenating twelve typical, extreme cold, and extreme warm months respectively to capture the variability within the ensemble of climate model simulations. 3. Moisture Reference year data are prepared for hygrothermal applications. The median ranked year in terms of MI is selected as the conditioning year and the 10% level year is selected as the extreme year for hygrothermal applications. 4. Overheating reference year data are prepared to support the evaluation of overheating risk in buildings. The method used is described in Laouadi et al. (2020). In this approach, reference years are first generated for each simulation run and each global warming level by identifying extreme years with a return period greater than 15.5 years, using maximum-value statistical distribution functions. Three reference years are generated to represent three types of heat waves: long, intense, and severe. This analysis is repeated for all 15 simulation runs. The final reference years are then selected from the 15 runs based on the maximum values of duration, intensity, and severity. The data are provided for a historical time-period: 1991-2021 and seven future time-periods coinciding with 0.5ºC, 1ºC, 1.5ºC, 2ºC, 2.5ºC, 3ºC, 3.5ºC of global warming.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 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) | 0.012 | 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".