Biomass burning and pollution aerosol over North America: Organic components and their influence on spectral optical properties and humidification response
Bibliographic record
Abstract
Thermal analysis of aerosol size distributions provided size resolved volatility up to temperatures of 400°C during extensive flights over North America (NA) for the INTEX/ICARTT experiment in summer 2004. Biomass burning and pollution plumes identified from trace gas measurements were evaluated for their aerosol physiochemical and optical signatures. Measurements of soluble ionic mass and refractory black carbon (BC) mass, inferred from light absorption, were combined with volatility to identify organic carbon at 400°C (VolatileOC) and the residual or refractory organic carbon, RefractoryOC. This approach characterized distinct constituent mass fractions present in biomass burning and pollution plumes every 5–10 min. Biomass burning, pollution and dust aerosol could be stratified by their combined spectral scattering and absorption properties. The “nonplume” regional aerosol exhibited properties dominated by pollution characteristics near the surface and biomass burning aloft. VolatileOC included most water‐soluble organic carbon. RefractoryOC dominated enhanced shortwave absorption in plumes from Alaskan and Canadian forest fires. The mass absorption efficiency of this RefractoryOC was about 0.63 m2 g−1 at 470 nm and 0.09 m2 g−1 at 530 nm. Concurrent measurements of the humidity dependence of scattering, γ, revealed the OC component to be only weakly hygroscopic resulting in a general decrease in γ with increasing OC mass fractions. Under ambient humidity conditions, the systematic relations between physiochemical properties and γ lead to a well‐constrained dependency on the absorption per unit dry mass for these plume types that may be used to challenge remotely sensed and modeled optical properties.
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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 | 0.000 | 0.000 |
| 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".