Increased Atmospheric Aridity and Reduced Precipitation Drive the 2023 Extreme Wildfire Season in Canada
Bibliographic record
Abstract
Abstract Canada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiometer burned area (BA) observations and machine learning methods. We found that the 2023 coast‐to‐coast wildfires raged a staggering area of 13.02 Mha, more than seven times the historical average (2001–2022). The extreme wildfires were predominantly driven by record‐breaking drought, characterized by heightened atmospheric aridity and reduced precipitation, with specific water deficit thresholds triggering sharp increases in BA. Over 80% of the 2023 wildfires occurred in Canada's permafrost regions, with burned areas increasing more than six‐fold across extensive permafrost, including Arctic regions. These wildfires are expected to accelerate permafrost degradation and considerable carbon release from thawing, presenting previously overlooked risks. Our results emphasize the urgent need for immediate climatic action to mitigate climate change and pressures from wildfire and permafrost degradation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".