Early Season 2023 Wildfires Generated Record‐Breaking Surface Ozone Anomalies Across the U.S. Upper Midwest
Bibliographic record
Abstract
Abstract During summer 2023 Canada experienced its most intense wildfire season on record. Smoke plumes from these fires advected across the United States (U.S.) Upper Midwest, producing regional scale surface enhancements of PM 2.5 and ozone, as recorded by the U.S. surface monitoring network. These events are notable because they occurred early in the fire season (May 15‐June 30), and they produced the highest regional‐scale surface ozone levels ever recorded across the northern tier of the U.S. during early (May–June) or late (July‐August) summer. Specifically, the Upper Midwest 50th ozone percentile was greater than in any other year since 1995, when the ozone monitoring network had sufficient coverage to assess regional‐scale ozone levels; the 90th percentile was the highest since 2002. Satellite and aircraft measurements demonstrate the availability of ozone precursors and ozone production within the smoke plumes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".