Chemical and dispersal characteristics of particulate emissions from forest fires in Siberia
Bibliographic record
Abstract
Approximately 20 experimental fires were conducted on forest plots of 1–4 ha each in 2000–07 in two types of boreal forests in central Siberia, and 18 on 6 × 12-m plots in 2008–10. These experiments were designed to mimic wildfires under similar burning conditions. The fires were conducted in prescribed conditions including full documentation on pre-fire weather, pre-fire and post-fire forest fuels, fire intensities, and other biological, physical and chemical parameters. The amount of particulate matter emitted during a typical fire averaged 0.6 t ha–1 and ranged within 0.2–1.0 t ha–1 depending on burning conditions. Particulates accounted for ~1–7% of the total mass of the consumed biomass during a typical forest fire (10–30 t ha–1 based on our data from 2000–07). Most of the particulate matter consists of organic substances, 77% on average, with a range of 70–90%. Elemental carbon averaged 8%, with a range of 2–18%. Trace element compositions and amounts of particulates indicate that there was no actual difference in the element emissions sampled from the fires conducted in the two forest types (6–8% in larch forest and 8% in pine forest). Most of the particulate matter, 90–95%, consists of submicrometre and near-micrometre particles ~0.1–5 μm in diameter.
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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 | 0.000 | 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".