Influence of Atlantic and Pacific Sea Surface Temperatures on Heat‐Related Mortality in the United States
Bibliographic record
Abstract
The frequency and magnitude of extreme summer temperature events in the United States have increased in the past few decades. Long-term exposure to extreme summer temperatures can be detrimental to human health, due to potential risks of dehydration and thermoregulation strains on the cardiovascular system, which may often lead to heat-related mortality (HRM). The summer climate of the United States is influenced by variability in Atlantic and Pacific sea surface temperatures, driven in part by Atlantic Multidecadal Oscillation (AMO) and El-Nino Southern Oscillation (ENSO), respectively. However, the influence of AMO and ENSO on HRM in the United States has not been investigated. Here the longest time series of HRM spanning the past five decades is analyzed in relation with AMO and ENSO. We find that HRM doubled in the early-1990s, coinciding with the positive phase of the AMO. Furthermore, we note a positive association between the variability in HRM and summer temperatures across all regions of the United States, with the strongest association found over the Southern United States. Therefore, this research suggests that variability in Atlantic and Pacific sea surface temperatures has both a nationwide and regional impact on HRM in the United States. Hence, by understanding variability in sea surface temperatures, the future burden of heat-attributed emergencies during extreme summer temperature events can be reduced not only for the United States, but also worldwide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".