Concentrations and sources of organic carbon aerosols in the free troposphere over North America
Bibliographic record
Abstract
Aircraft measurements of water‐soluble organic carbon (WSOC) aerosol over NE North America during summer 2004 (ITCT‐2K4) are simulated with a global chemical transport model (GEOS‐Chem) to test our understanding of the sources of organic carbon (OC) aerosol in the free troposphere (FT). Elevated concentrations were observed in plumes from boreal fires in Alaska and Canada. WSOC aerosol concentrations outside of these plumes average 0.9 ± 0.9 μg C m−3 in the FT (2–6 km). The corresponding model value is 0.7 ± 0.6 μg C m−3, including 42% from biomass burning, 36% from biogenic secondary organic aerosol (SOA), and 22% from anthropogenic emissions. Previous OC aerosol observations over the NW Pacific in spring 2001 (ACE‐Asia) averaged 3.3 ± 2.8 μg C m−3 in the FT, compared to a model value of 0.3 ± 0.3 μg C m−3. WSOC aerosol concentrations in the boundary layer (BL) during ITCT‐2K4 are consistent with OC aerosol observed at the IMPROVE surface network. The model is low in the boundary layer by 30%, which we attribute to secondary formation at a rate comparable to primary anthropogenic emission. Observed WSOC aerosol concentrations decrease by a factor of 2 from the BL to the FT, as compared to a factor of 10 decrease for sulfate, indicating that most of the WSOC aerosol in the FT originates in situ. Despite reproducing mean observed WSOC concentrations in the FT to within 25%, the model cannot account for the variance in the observations (R = 0.21). Covariance analysis of FT WSOC aerosol with other measured chemical variables suggests an aqueous‐phase mechanism for SOA generation involving biogenic precursors.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".