Distribution and characteristics of lightning-ignited wildfires in boreal forests – the BoLtFire database
Bibliographic record
Abstract
Abstract. The frequency and severity of fire weather have been projected to increase under climate change, particularly in high-latitude boreal forests. Lightning, a key ignition source globally, is also expected to become more frequent with climate change and could significantly increase burn area. Current research on lightning-ignited wildfire (LIWs) has a long history in boreal ecosystems but has typically focused on North America due to better data availability, while the lack of publicly available data for Eurasia has hindered our comprehensive understanding of important characteristics of LIWs, such as holdover time, lightning ignition efficiency, frequency, and spatial distribution in boreal forests. This study introduces the temporal minimum distance (TMin) method, a novel approach to matching lightning strikes with wildfires without requiring ignition location that outperformed current methodologies. As a result, we developed a comprehensive dataset of lightning-ignited wildfires across the entire boreal forest from 2012 to 2022, encompassing 6902 fires, 4201 in Eurasia and 2701 in North America, each over 200 ha in size. This dataset (https://doi.org/10.5281/zenodo.14940326, Engle et al., 2024) provides new opportunities to model the ignition and spread dynamics of boreal wildfires and offers deeper insights into lightning-driven fire activity globally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".