Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan
Bibliographic record
Abstract
Abstract Deforestation in Indonesia in recent decades has made increasingly large parts of the region vulnerable to fires. Burning is particularly widespread in deforested peatlands, and it leads to globally significant carbon emissions. Here we use satellite-based observations to assess loss and fragmentation of primary forests and associated changes in fire regimes in Sumatra and Kalimantan between 2001 and 2019. We find that fires did not penetrate undisturbed primary forest areas deeper than two kilometres from the forest edge irrespective of drought conditions. However, fire-resistant forest now covers only 3% of peatlands and 4.5% of non-peatlands; the majority of the remaining primary forests are severely fragmented or degraded due to proximity to the forest edge. We conclude that protection and regeneration of the remaining blocks of contiguous primary forest, as well as peatland restoration, are urgently needed to mitigate the impacts of potentially more frequent fire events under future global warming.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".