Extreme wildfires in Canada and their contribution to global loss in tree cover and carbon emissions in 2023
Bibliographic record
Abstract
Abstract Canadian wildfires in 2023 were record breaking with wide‐reaching impacts on people, nature, and climate. Extreme heat and low rainfall associated with climate change led to unprecedented forest fires that released enormous amounts of carbon as they burned. This study used data on fire‐driven tree cover loss and forest carbon fluxes to estimate the total extent of stand‐replacing forest fires and their associated carbon emissions. We found that the 2023 Canadian wildfires burned nearly 7.8 million hectares of forest and accounted for more than a quarter of all tree cover loss globally. Furthermore, forests impacted by wildfires emitted nearly 3 billion tons of CO 2 or about 25% more carbon than all primary tropical tree cover loss that year. These results have important implications for global carbon budgets because emissions from these wildfires will largely be excluded from official greenhouse gas reporting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".