Substantial and increasing global losses of timber-producing forest due to wildfires
Bibliographic record
Abstract
Abstract One-third of global forest is harvested for timber, generating ~US$1.5 trillion annually. High-severity wildfires threaten this timber production. Here we combine global maps of logging activity and stand-replacing wildfires to assess how much timber-producing forest has been lost to wildfire this century, and quantify spatio-temporal changes in annual area lost. Between 2001 and 2021, 18.5–24.7 million hectares of timber-producing forest—an area the size of Great Britain—experienced stand-replacing wildfires, with extensive burning in the western USA and Canada, Siberian Russia, Brazil and Australia. Annual burned area increased significantly throughout the twenty-first century, pointing to substantial wildfire-driven timber losses under increasingly severe climate change. To meet future timber demand, producers must adopt new management strategies and emerging technologies to combat the increasing threat of wildfires.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 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".