Fuel characteristics, loads and consumption in Scots pine forests of central Siberia
Bibliographic record
Abstract
Abstract Forest fuel investigations in central and southern Siberian taiga of Scots pine forest stands dominated by lichen and feather moss ground vegetation cover revealed that total aboveground biomass varied from 13.1 to 21.0 kg/m2. Stand biomass was higher in plots in the southern taiga, while ground fuel loads were higher in the central taiga. We developed equations for fuel biomass (both aerial and ground) that could be applicable to similar pine forest sites of Central Siberia. Fuel loading variability found among plots is related to the impact and recovery time since the last wildfire and the mosaic distribution of living vegetation. Fuel consumption due to surface fires of low to high-intensities ranged from 0.95 to 3.08 kg/m2, that is, 18–74% from prefire values. The total amount of fuels available to burn in case of fire was up to 4.5–6.5 kg/m2. Moisture content of fuels (litter, lichen, feather moss) was related to weather conditions characterized by the Russian Fire Danger Index (PV-1) and FWI code of the Canadian Forest Fire Weather Index System. The data obtained provide a strong foundation for understanding and modeling fire behavior, emissions, and fire effects on ecosystem processes and carbon stocks and could be used to improve existing global and regional models that incorporate biomass and fuel characteristics.
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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 | 0.001 |
| 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) | 0.001 | 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".