An estimate of carbon emissions from 2004 wildfires across Alaskan Yukon River Basin
Bibliographic record
Abstract
BACKGROUND: Wildfires are an increasingly important component of the forces that drive the global carbon (C) cycle and climate change as progressive warming is expected in boreal areas. This study estimated C emissions from the wildfires across the Alaskan Yukon River Basin in 2004. We spatially related the firescars to land cover types and defined the C fractions of aboveground biomass and the ground layer (referring to the top 15 cm organic soil layer only in this paper) consumed in association with land cover types, soil drainage classes, and the C stocks in the ground layer. RESULTS: The fires led to a burned area of 26,500 km2 and resulted in the total C emission of 81.1 +/- 13.6 Tg (Tg, Teragram; 1 Tg = 1012 g) or 3.1 +/- 0.7 kg C m-2 burned. Of the total C emission, about 73% and 27% could be attributed to the consumption of the ground layer and aboveground biomass, respectively. CONCLUSION: The predominant contribution of the ground layer to the total C emission implies the importance of ground fuel management to the control of wildfires and mitigation of C emissions. The magnitude of the total C emission depends on fire extent, while the C loss in kg C m-2 burned is affected strongly by the ground layer and soil drainage condition. The significant reduction in the ground layer by large fires may result in profound impacts on boreal ecosystem services with an increase in feedbacks between wildfires and 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 | 0.000 | 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) | 0.000 | 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".