Adaptive Capacity to Extreme Heat: Results from a Household Survey in Houston, Texas
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
Abstract Extreme heat is the leading cause of weather-related mortality in the United States, suggesting the necessity for better understanding population vulnerability to extreme heat. The work presented here is part of a larger study examining vulnerability to extreme heat in current and future climates [System for Integrated Modeling of Metropolitan Extreme Heat Risk (SIMMER)] and was undertaken to assess Houston, Texas, residents’ adaptive capacity to extreme heat. A comprehensive, semistructured survey was conducted by telephone at 901 households in Houston in 2011. Frequency and logistic regression analyses were conducted. Results show that 20% of the survey respondents reported heat-related symptoms in the summer of 2011 despite widespread air conditioning availability throughout Houston. Of those reporting heat-related symptoms experienced in the home (n = 56), the majority could not afford to use air conditioning because of the high cost of electricity. This research highlights the efficacy of community-based surveys to better understand adaptive capacity at the household level; this survey contextualizes population vulnerability and identifies more targeted intervention strategies and adaptation actions.
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 | 0.001 | 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