Estimating fire emissions and disparities in boreal Siberia (1998–2002)
Why this work is in the frame
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Bibliographic record
Abstract
In the biomass, soils, and peatlands of Siberia, boreal Russia holds one of the largest pools of terrestrial carbon. Because Siberia is located where some of the largest temperature increases are expected to occur under current climate change scenarios, stored carbon has the potential to be released with associated changes in fire regimes. Our concentration is on estimating a wide range of current and potential emissions from Siberia on the basis of three modeled scenarios. An area burned product of Siberia is introduced, which spans from 1998 through 2002. Emissions models are spatially explicit; therefore area burned is extracted from associated ecoregions for each year. Carbon consumption estimates are presented for 23 unique ecoregions across Siberia, which range from 3.4 to 75.4 t C ha −1 for three classes of severity. Total direct carbon emissions range from the traditional scenario estimate of 116 Tg C in 1999 (6.9 M ha burned) to the extreme scenario estimate of 520 Tg C in 2002 (11.2 M ha burned), which are equivalent to 5 and 20%, respectively, of total global carbon emissions from forest and grassland burning. Our results suggest that disparities in the amount of carbon stored in unique ecosystems and the severity of fire events can affect total direct carbon emissions by as much as 50%. Additionally, in extreme fire years, total direct carbon emissions can be 37–41% greater than in normal fire years, owing to increased soil organic matter consumption. Mean standard scenario estimates of CO 2 (555–1031 Tg), CO (43–80 Tg), CH 4 (2.4–4.5 Tg), TNMHC (2.2–4.1 Tg), and carbonaceous aerosols (4.6–8.6 Tg) represent 10, 15, 19, 12 and 26%, respectively, of the global estimates from forest and grassland burning. Accounting for smoldering combustion in soils and peatlands results in increases in CO, CH 4 , and TNMHC and decreases in CO 2 emitted from fire events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it