Global risk of wildfire across timber production systems
Bibliographic record
Abstract
Timber is worth $1.5 trillion US Dollars annually with demand rising, but wildfires increasingly threaten production. Plantations occupy 3% of forests globally and produce 33% of the world's timber, but a critical question is whether they are more vulnerable to stand-replacing wildfires than natural production forests. We combine forest management and wildfire data to estimate that 15.7 (14.7-16.7) million hectares of natural production forests and 1.4 (1.26-1.64) million hectares of plantations suffered stand-replacing wildfires between 2015 and 2022. Using statistical matching for 17 countries representing 50% of global production and 75% of burned timber-producing forest, we find plantations in temperate regions were twice as likely to suffer stand-replacing wildfires than natural production forests, including in vital timber-producing nations like China and Russia. Plantations in tropical regions showed no clear effect, with national differences ranging from 75% lower to 58% higher risk of burning. Given increasing global reliance on plantation timber, preventing wildfires through landscape-level planning, fire management, and increased plantation diversity is critical for global wood security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".