Comment on egusphere-2024-1781
Bibliographic record
Abstract
Abstract. Canadian wildfires transport large concentrations of particulate matter into the United States, leading to various impacts on surface temperature, radiation balance, visibility, and exacerbating pollution-related respiratory conditions. Using a combination of surface, satellite and numerical models, this study quantifies the increase in surface fine particulate matter (PM2.5) in the Continental United States due to long-range transported smoke from Canadian wildfires during a wildfire episode from August 9th to 25th, 2018. As a widely used indicator of surface pollution levels, satellite-retrieved AOD can provide crucial information on columnar pollution mass. However, the daily spatial coverage of satellite AOD is restricted due to cloud cover. In order to quantify the daily changes of surface pollution, we fill in the AOD gaps by utilizing simulated 10-m spatial resolution AOD from a chemistry transport model (CTM). In addition, different processes affecting smoke vertical and horizontal transport are examined using two CTM simulations. Meteorological variables associated with these processes are then selected along with the gap-filled AOD product to assess the surface pollution using geographically weighted regression (GWR) and random forest (RF) models. It is found that synoptic pressure systems dominate the horizontal transport of Canadian smoke, and the ascending air and robust winds from a low-pressure system contributes to the long distances of the transport path. Other processes affecting the vertical distribution of pollutants, including boundary layer entrainment, precipitation and terrain-induced vertical mixing are also analyzed in this paper. Our results show that Canadian fires caused a substantial increase in total PM2.5, reaching up to 13 % (62 μgm−3) across different US EPA regions during 2018 August wildfire event.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.011 |
| Insufficient payload (model declined to judge) | 0.369 | 0.283 |
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".