Thermal resiliency of single-family housing stock under extreme hot and cold conditions
Bibliographic record
Abstract
The building sector has gained attention due to its vulnerability to hazards such as heat waves in summer and power outages in winter, which have led to significant human health issues, including deaths. Thermal resiliency, which refers to a building’s capacity to cope and recover from weather-related events affecting indoor thermal conditions, lacks a systematic assessment approach considering future climate changes. This paper presents a framework for thermal resiliency assessment of buildings under future climatic conditions. The framework evaluates passive survivability under both hot and cold extreme events during a power outage or an HVAC system failure. A case study was conducted by considering code-compliant residential buildings located in different climatic regions. The study indicates that code-compliant buildings may overheat during hot extreme events without air conditioning in the future climate; however, severe indoor conditions can be avoided with passive measures like natural ventilation. The thermal resiliency of buildings under extreme conditions in cold regions is not adequate, as the buildings can reach severe indoor conditions within a four-day power outage, even with passive measures such as movable insulations. The proposed framework and study’s findings can serve as a valuable resource for policy-makers and researchers in developing climate adaptation measures.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".