Global climate change below 2 °C avoids large end century increases in burned area in Canada
Bibliographic record
Abstract
Wildfire impacts the global carbon cycle, property, harvestable timber, and public health. Canada saw a record fire season in 2023 with 14.9 Mha burned-over seven times the 1986-2022 average of 2.1 Mha. Here we utilize a new process-based wildfire module that explicitly represents fire weather, fuel type and availability, ignition sources, fire suppression, and vegetation's climate response to project the future of wildfire in Canada. Under rapid climate change (shared socioeconomic pathway [SSP] 370 & 585) simulated annual burned area in the 2090 s reaches 10.2 ± 2.1 to 11.7 ± 2.4 Mha, approaching the 2023 fire season total. However, climate change below a 2 °C global target (SSP126), keeps the 2090 s area burned near modern (2004-2014) norms. The simulated area burned and carbon emissions are most sensitive to climate drivers and lightning but future lightning activity is a key uncertainty.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".