Dispersal and morphological characteristics of smoke particulate emission from fires in the boreal forests of Siberia
Bibliographic record
Abstract
About 10–14 million hectares of Siberian boreal forests burn annually. These forest fires, which amount to 300–500 million tons of biomass each year, result in smoke emission into the atmosphere. Direct measurements of smoke emissions conducted in several natural fire experiments at a taiga in Krasnoyarsk Territory between 2000–2009 have shown that the total amount of particulate emission from the fire is estimated to be 0.2–1 t/ha. These values represent 1–7% of the total biomass consumed during a typical forest fire in Siberia (15–30 t/ha); the remaining 93–99% of the burnt biomass are gaseous combustion products. Data on the disperse characteristics of particulate smokes, averaged over 16 natural fire experiments from 2007–2009, have shown that (89 ± 8)% of the total aerosol matter are in particles with aerodynamical diameters of less than 3 μm, (7 ± 6)% are in particles of 3–5 μm, and the remaining 5–10% of material is in particles larger than 7 μm. The morphological structure of the smoke particles indicates that submicron particles are formed due to condensation of organic vapors directly over a combustion zone, followed by their coagulation into particles of 1–3 μm. The elemental composition of the fine fraction of smoke emission, measured with the use of X-ray fluorescence with synchroton radiation excitation, is demonstrated to be used for discrimination between the sources of the elements.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".