Characterization of a large biogenic secondary organic aerosol event from eastern Canadian forests, Atmos
Bibliographic record
Abstract
Measurements of aerosol composition, volatile organic compounds, and CO are used to determine biogenic secondary organic aerosol (SOA) concentrations at a rural site 70 km north of Toronto. These biogenic SOA levels are many times higher than past observations and occur during a period of increasing temperatures and outflow from Northern Ontario and Quebec forests in early summer. A regional chemical transport model approximately predicts the event timing and accurately predicts the aerosol loading, identifying the precursors as monoterpene emissions from the coniferous forest. The agreement between the measured and modeled biogenic aerosol concentrations contrasts with model underpredictions for polluted regions. Correlations of the oxygenated organic aerosol mass with tracers such as CO support a secondary aerosol source and distinguish biogenic, pollution, and biomass burning periods during the field campaign. Using the Master Chemical Mechanism, it is shown that the levels of CO observed during the biogenic event are consistent with a photochemical source arising from monoterpene oxidation. The biogenic aerosol mass correlates with satellite measurements of regional aerosol optical depth, indicating that the event extends across the eastern Canadian forest. This regional event correlates with increased temperatures, indicating that temperature-dependent forest 1 emissions can significantly affect climate through enhanced direct optical scattering and higher cloud condensation nuclei numbers. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".