An empirical review of methods to assess overheating in buildings in the context of changes to extreme heat events
Why this work is in the frame
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Bibliographic record
Abstract
Under climate change, extreme heat events are projected to become more frequent and intense. With people spending approximately 90% for their time indoors and buildings having long lifetimes, it is important that the built environment is resilient to these changes. Current methods to assess building performance in a future climate typically use morphed weather files and annual metrics. We compare 30 metrics and 2 weather data sources to assess and improve the representation of extreme heat events in building simulation. We show that morphing an extreme observed year may not necessarily result in an equally extreme year under the future climate and that current annual metrics do not correlate well with heatwave severity. We suggest that weather data from climate models is more robust in representing future weather for the UK and explore the recent UKCP18 data. We propose novel metrics which are able to capture heatwave severity inside buildings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it