The motley drivers of heat and cold exposure in 21st century US cities
Bibliographic record
Abstract
We use a suite of decadal-length regional climate simulations to quantify potential changes in population-weighted heat and cold exposure in 47 US metropolitan regions during the 21st century. Our results show that population-weighted exposure to locally defined extreme heat (i.e., "population heat exposure") would increase by a factor of 12.7-29.5 under a high-intensity greenhouse gas (GHG) emissions and urban development pathway. Additionally, end-of-century population cold exposure is projected to rise by a factor of 1.3-2.2, relative to start-of-century population cold exposure. We identify specific metropolitan regions in which population heat exposure would increase most markedly and characterize the relative significance of various drivers responsible for this increase. The largest absolute changes in population heat exposure during the 21st century are projected to occur in major US metropolitan regions like New York City (NY), Los Angeles (CA), Atlanta (GA), and Washington DC. The largest relative changes in population heat exposure (i.e., changes relative to start-of-century) are projected to occur in rapidly growing cities across the US Sunbelt, for example Orlando (FL), Austin (TX), Miami (FL), and Atlanta. The surge in population heat exposure across the Sunbelt is driven by concurrent GHG-induced warming and population growth which, in tandem, could strongly compound population heat exposure. Our simulations provide initial guidance to inform the prioritization of urban climate adaptation measures and policy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".