Measurement of Physicochemical Properties of Black Carbon and Brown Carbon and the Impacts of Canada Record-Breaking Wildfires in Summer 2023 
Bibliographic record
Abstract
Wildfires have become more prevalent in recent years because of climate change. Meanwhile wildfires, as a major type of biomass burning, could emit a large amount of black carbon (BC) and brown carbon (BrC) to the atmosphere. Since BC and BrC play important roles in climate change, air pollution and human health issues, it is necessary to research their physicochemical properties to evaluate their impacts on urban areas. Here we present BC mass concentration and absorption coefficients measured by aethalometer (AE43), combing with the chemical constitutions acquired by GC-MS, during the record-breaking 2023 wildfire season in Canada. The back-trajectory analysis indicated that the smoke mainly came from north Quebec where the wildfires took place. We demonstrated how BC and BrC emitted by wildfires could affect urban regions after long-range transport.
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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 | 0.001 | 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) | 0.001 | 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".