Radiative Properties of Numerically Generated Fractal Soot Aggregates: The Importance of Configuration Averaging
Bibliographic record
Abstract
The radiative properties of numerically generated fractal soot aggregates were studied using the numerically accurate generalized multi-sphere Mie-solution method. The fractal aggregates investigated in this study contain from 10 to 600 primary particles of 30 nm in diameter. These fractal aggregates were numerically generated using a combination of the particle-cluster and cluster-cluster aggregation algorithms with fractal parameters representing flame generated soot. Ten different realizations were obtained for a given aggregate size measured by the number of primary particles. The wavelength considered is 532 nm and the corresponding size parameter of primary particle is 0.177. Attention is paid to the effect of different realizations of a fractal aggregate with identical fractal dimension, prefactor, primary particle diameter, and the number of primary particles on its orientation-averaged radiative properties. Most properties of practical interest exhibit relatively small variation with aggregate realization. However, other scattering properties, especially the vertical-horizontal differential scattering cross section, are very sensitive to the variation in geometrical configuration of primary particles. Orientation-averaged radiative properties of a single aggregate realization are not always sufficient to represent the properties of random-oriented ensemble of fractal aggregates.
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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 | 0.001 | 0.002 |
| 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".