Canadian wildfires of Summer 2023:  high smoke episodes over Central Europe - observations and GEM-AQ model results.
Bibliographic record
Abstract
In 2023, Canada faced the threat of wildfires, a recurring environmental challenge exacerbated by factors such as climate change and dry conditions. The wildfires likely posed significant challenges to various regions, leading to evacuations, property damage, and adverse effects on air quality. Government agencies and firefighting teams were likely mobilized to contain the spread of the fires and protect affected communities. Copernicus Atmosphere Monitoring Service (CAMS) provides products related to emissions from wildfires (PMWF – particular matter from wildfires). Based on CAMS data specific periods were selected. The GEM-AQ model, which is a part of the CAMS ensemble, was run on a global grid to reproduce the hemispheric transport of smoke plum. Model results were compared against satellite measurement (TROPOMI – aerosol index and aerosol layer height) and with available in-situ observations (PolandAOD network). We will present the evolution of transport episodes over Poland as well as the analysis of the model performance in terms of timing and height of the aerosol plume observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".