Estimating Greenhouse Gas Emissions From Peat Combustion in Wildfires on Indonesian Peatlands, and Their Uncertainty
Bibliographic record
Abstract
Abstract Peatlands play an important role as carbon pools, storing a third of the world's soil carbon. However, peatlands in Southeast Asia have suffered from depletion due to economic pressure and the demand for natural resources, often caused by land use changes and fires. Usually, land preparation requires drainage and fires, resulting in major greenhouse gas (GHG) emissions into the atmosphere. In this work, we propose a general equation to estimate GHG emissions from fires on peatlands. The contribution of each parameter to the variance of the estimated GHG emissions was also evaluated. We used Monte Carlo simulation, meta‐analyses, and an analytical expression of variance. GHG emissions of a single fire episode were estimated at 842 Mg ha−1 CO2 eq. with a standard deviation of 466 Mg ha−1 CO2 eq. The parameter contributing most to variance was the depth of burn, at 94.2%, followed by bulk density, at 5.5%, and emission factors, at 0.3%. Our estimated GHG emissions were close to the amount estimated from the default values provided by the IPCC, strengthening confidence in the IPCC methodology. When the depth of burn was assessed by remote sensing, the parameter that most contributed to variance became the fire‐damaged area, followed by the depth of burn. The contribution of each parameter to variance, as estimated in this study, made it possible to prioritize the effort in uncertainty reduction. Combining Monte Carlo simulation and an analytical expression of variance could be a promising way of obtaining more reliable confidence intervals.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".