Fire emissions estimates in Siberia: evaluation of uncertainties in area burned, land cover, and fuel consumption
Bibliographic record
Abstract
Boreal forests constitute the world's largest terrestrial carbon pools. The main natural disturbance in these forests is wildfire, which modifies the carbon budget and atmosphere, directly and indirectly. Wildfire emissions in Russia contribute substantially to the global carbon cycle and have potentially important feedbacks to changing climate. Published estimates of carbon emissions from fires in Russian boreal forests vary greatly depending on the methods and data sets used. We examined various fire and vegetation products used to estimate wildfire emissions for Siberia. Large (up to fivefold) differences in annual and monthly area burned estimates for Siberia were found among four satellite-based fire data sets. Official Russian data were typically less than 10% of satellite estimates. Differences in the estimated proportion of annual burned area within each ecosystem were as much as 40% among five land-cover products. As a result, fuel consumption estimates would be expected to vary widely (3%–98%) depending on the specific vegetation mapping product used and as a function of weather conditions. Verification and validation of burned area and land-cover data sets along with the development of fuel maps and combustion models are essential for accurate Siberian wildfire emission estimates, which are central to balancing the carbon budget and assessing feedbacks to climate change.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".