Building indoor overheating and impact of local extreme heat conditions
Bibliographic record
Abstract
Fast urbanizations have been adding to natural land surfaces with large vertical structures and imperious grounds creating higher temperatures in urban areas than the surroundings. Extreme heat events in the urban areas are also projected to occur more frequently and last longer as a result of global warming. Therefore, the evaluation of the impacts from these future weather events on urban dwellers and the understanding of building resilience in terms of energy consumption becomes important at the moment. In this study, a 1 km resolution Weather Research and Forecasting (WRF) model is developed for the Montreal city and validated by field monitored data. The validated model will be used to dynamically downscale future projections of climate made around the city to evaluate the overheating conditions based on building energy simulations. The indoor thermal conditions of 15 hospital and 12 school buildings are evaluated using modified DOE reference model in Energyplus according to the current National Building Code of Canada 2015. The observed weather data at five existing weather stations in the Montreal area is used for the validation of the mesoscale model. The model selections in the WRF simulation are discussed according to the validation results. With the final outputs, relevant climate-adaptation technologies can be selected for similar building types to overcome potential overheating risks for occupants and reduce building energy demands for future buildings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".