Angularly-resolved measurements of light scattering by smoke from wildfires during FIREX-AQ
Bibliographic record
Abstract
The open burning of biomass fuels is an important source of aerosols because they contribute significantly to the pre-industrial radiative forcing budget and they are a large source of aerosol in the modern era that is anticipated to increase due to climate change. However, the optical properties of smoke have been shown to be complex and variable, which in turn complicates a) the retrieval of aerosol properties using remote measurements and b) the estimation of the direct radiative forcing caused by smoke. During the FIREX-AQ aircraft campaign, we measured the angular distribution of light (i.e. scattering phase function) scattered by smoke in situ using the NOAA Laser Imaging Nephelometer. We then used collocated measurements of the particle size distribution and literature values of the complex refractive indices to calculate expected phase functions using Mie theory. When comparing the measured versus calculated phase functions, we see there is more backscattered light in the measurements. This enhanced backscatter has two important repercussions. First, when the measured phase function is used with an open source algorithm (GRASP) to retrieve the particle mode size, we find that the algorithm tends to undersize the particles by about 10%. Second, when the enhanced backscatter is included in a simple radiative transfer model, we observe an additional 20% cooling effect from fresh smoke.
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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 | 0.000 |
| 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) | 0.001 | 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".