Predictability of carbon emissions from biomass burning in Indonesia from 1997 to 2006
Bibliographic record
Abstract
Drought and sea surface temperature were examined as the causes of severe biomass burning C emissions in Indonesia for 1997–2006, obtained from the Global Fire Emissions Database. Eighteen predictor variables were considered under log linear and piecewise regression models. The predictor variables considered were precipitation totals of up to 6 months, output from two soil moisture models, and sea surface temperature (SST) indicators reflecting El Niño and Indian Ocean Dipole strength. Nonparametric bootstrap techniques were used to estimate confidence intervals for predictability and thresholds below which severe C emissions are likely. Across equatorial Southeast Asia, the best predictor was 3‐month total precipitation, which explained 79% of variance in C emissions. When considered individually, and with the incorporation of satellite precipitation estimates, predictability for southern Sumatra and southern Kalimantan improved to 97% and 92%, respectively, using 4‐month total precipitation. There is a high risk of severe burning when 4‐month precipitation falls below thresholds of 350 mm in southern Sumatra and 650 mm in southern Kalimantan and when 6‐month precipitation falls below 900 mm in Papua. In general, simple precipitation totals outperformed more complicated soil moisture models and SST‐based indices. Physically, seasonal precipitation controls fire emissions through its regulation of groundwater level and, hence, the amount of peat available for drying. Seasonal precipitation, in turn, is strongly influenced by SST patterns in the tropical Pacific and Indian oceans. The most severe drought and fire events appear equally influenced by Indian Ocean Dipole events and El Niño events.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".