Modelling the optical properties of fresh biomass burning aerosol produced in a smoke chamber: results from the EFEU campaign
Bibliographic record
Abstract
Abstract. A better characterisation of the optical properties of biomass burning aerosol as a function of the burning conditions is required in order to quantify their effects on climate and atmospheric chemistry. Controlled laboratory combustion experiments with different fuel types were carried out at the combustion facility of the Max Planck Institute for Chemistry (Mainz, Germany) as part of the "Impact of Vegetation Fires on the Composition and Circulation of the Atmosphere" (EFEU) project. The combustion conditions were monitored with concomitant CO2 and CO measurements. The mass scattering efficiencies of 8.9±0.2 m2 g−1 and 9.3±0.3 m2 g−1 obtained for aerosol particles from the combustion of savanna grass and an African hardwood (musasa), respectively, are larger than typically reported mainly due to differences in particle size distribution. The photoacoustically measured mass absorption efficiencies of 0.51±0.02 m2 g−1 and 0.50±0.02 m2 g−1 were at the lower end of the literature values. Using the measured size distributions as well as the mass scattering and absorption efficiencies, Mie calculations provided effective refractive indices of 1.60−0.010i (savanna grass) and 1.56−0.010i (musasa) (λ=0.55 μm). The apparent discrepancy between the low imaginary part of the refractive index and the high apparent elemental carbon (ECa) fractions (8 to 15%) obtained from the thermographic analysis of impactor samples can be explained by a positive bias in the elemental carbon data due to the presence of high molecular weight organic substances. Potential artefacts in optical properties due to instrument bias, non-natural burning conditions and unrealistic dilution history of the laboratory smoke cannot be ruled out and are also discussed in this study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".