Local power outages, heat, and community characteristics in New York City
Bibliographic record
Abstract
Electrical power outages are of increasing interest to US urban scholars, government officials and stakeholders, as they have increased in number and duration with significant health and economic, among other, impacts. This analysis examines reports of power outages in New York City in relation to socio-economic and health characteristics of neighborhoods. Using the city's 311-call database we examine complaint calls for power outages from 2014 to 2022. While 311-calls for power outages occur all year long, volume trended higher during the warmer months (June, July and August), and as minimum daily temperatures exceeded 20°C (68°F), the number of calls increased dramatically. Spatial clusters of high call areas were in Census tracts with high energy burdens, lower-income households, and high percentages of people of color. Furthermore, we found the higher call areas were associated with higher vulnerability to heat-exacerbated deaths. As climate change is expected to raise temperatures and increase the frequency and intensity of heat waves around the world, and as power outages are becoming more common, these findings will help to provide guidance for adaptation and energy reliability policies in New York City and have implications for other cities globally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".