Aerosol optical characterization by nephelometer and lidar: The Baltimore Supersite experiment during the Canadian forest fire smoke intrusion
Bibliographic record
Abstract
High spatial and temporal resolution elastic backscatter lidar data from Baltimore are analyzed with a near‐end approach to estimate vertical profiles of the aerosol extinction coefficient. The near‐end approach makes use of the (1) aerosol scattering coefficient measured at the surface with a nephelometer (0.530 μm), (2) surface level particle size distribution, and (3) refractive index calculated using Mie theory to estimate the aerosol extinction coefficient boundary condition for the lidar equation. There was a broad range of atmospheric turbidity due to a strong haze event, which occurred because of smoke transport from Canadian forest fires, and led to a wide range of observed atmospheric properties. The index of refraction for aerosols estimated during the entire study period is 1.5–0.47 i, which is typical for soot. The measured surface level aerosol scattering coefficient ranged from σp = 0.002 to σp = 0.541 km−1, and the computed aerosol extinction coefficient spanned values κp = 0.01 to κp = 1.05 km−1. The derived mass concentration and the mass scattering ranges were 3.96–194 μg m−3 and 0.05–3.260 m2g−1, respectively. The aerosol optical properties were dominated by light absorption by soot.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".