Origin of Smoke in the Record‐Breaking Air‐Pollution Event in New York, June 2023
Bibliographic record
Abstract
ABSTRACT During the fire season of 2023, extreme continuous wildfires in Canada exported smoke to distant areas. On June 6–8, record‐breaking smoke concentrations impacted human health and the environment in New York City (NYC) and its surroundings. In this work, for the first time, we incorporate Lagrangian airmass trajectories with Copernicus Atmospheric Monitoring Service (CAMS) forecasts to trace back the origin of the smoke in NYC and identify the weather systems governing its transport. We locate the main smoke plume which originated from fires in Quebec. The smoke traveled at a height of about 500 hPa southward and descended slantwise to NYC behind a deep cyclone over the east coast. A second peak in smoke concentration in NYC emerged by air that circulated around the cyclone back to the city, collecting smoke again from the fires in Quebec. Smoke from the major fires in western Canada did not contribute significantly to the NYC event but was transported at tropopause level toward Europe. The findings highlight the critical role of synoptic‐scale systems in the transport of wildfire smoke.
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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 | 0.001 |
| 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) | 0.002 | 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".