Ten questions concerning thermal resilience of buildings and occupants for climate adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With climate change leading to more frequent, more intense, and longer durations of extreme weather events such as heat waves and cold snaps, it is essential to maintain safe indoor environmental conditions for occupants during such events, which may coincide with, or even cause, power outages that expose residents to health risks. Analyzing the impacts of extreme weather events on the thermal resilience of buildings can help stakeholders (including occupants) understand the risk and inform them about mitigation and adaptation actions. Moreover, analyzing the technological, social and policy dimensions of thermal resilience is critical for climate-proofing buildings. This paper presents 10 questions that highlight the most important issues regarding the thermal resilience of buildings for occupants in the face of climate change. The proposed questions and answers aim to provide insights into current and future building thermal resilience research and applications, and more importantly to inspire new significant questions in the field.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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