Co-occurrence of extremes in surface ozone, particulate matter, and temperature over eastern North America
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
Significance Exposure to extreme temperatures and high levels of the pollutants ozone and particulate matter poses a major threat to human health. Heat waves and pollution episodes share common underlying meteorological drivers and thus often coincide, which can synergistically worsen their health impacts beyond the sum of their individual effects. Furthermore, there is evidence that pollution episodes and heat waves will worsen under future climate change, making it imperative to understand the nature of their co-occurrence. In this paper, using 15 years of surface observations over the eastern United States and Canada, we show that the extremes cluster together in often overlapping large-scale episodes, and that the largest episodes have the hottest temperatures and highest levels of pollution.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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