Quantifying the Fractal Dimension and Morphology of Individual Atmospheric Soot Aggregates
Bibliographic record
Abstract
Abstract The complex morphology of soot aggregates is a major source of uncertainty in evaluating their warming effects in the atmosphere. Fractal dimension (D f ) is a key parameter in quantifying the morphology of soot particles. Previous studies are mostly based on manual identification of soot monomers in electron microscopic images and are hard to provide comparable results in determination of D f . Here we develop a novel image recognition technique to automatically determine the D f of individual soot aggregates from electron microscopy images. The novel method has been shown to be able to trace the small change of the soot D f from an urban tunnel (1.61 ± 0.19) to its exit (1.70 ± 0.15). By applying this new method, we show a substantial difference in average D f of soot particles emitted from vehicles (1.66 ± 0.17) than from biomass burning (1.75 ± 0.18) and coal burning (1.76 ± 0.18). Average D f of soot from an urban atmosphere (1.77 ± 0.18) is close to that from biomass and coal combustion but much lower than that from a rural atmosphere (1.85 ± 0.13). In summary, the new technique provides an automatic, accurate and reliable quantification of soot morphology D f , enabling an improved understanding of soot aging processes and a more accurate modeling of soot impact on their climate.
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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) | 0.000 | 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".