Wildfire ignition from forestry machines in boreal Sweden
Bibliographic record
Abstract
Several large Swedish wildfires during recent decades were caused by forestry machinery in operation, fires for which there is still no characterisation. We combined 18 years of data on dispatches, weather and fire danger and interviewed forestry workers to understand the spatial, temporal and weather distributions of these fires, and their underlying mechanisms. We estimate the average annual number of ignitions from forestry machinery in Sweden at 330–480 (2.0 ± 0.4 ignitions per 1000 ha clear-felling) of which 34.5 led to firefighter dispatches, constituting 2.2% of all forest fire dispatches and 40% of area burnt. Soil scarification causes the most ignitions and the main mechanism is likely high-inertia contact between discs and large stones, causing sparks igniting dry humus or moss, countering reports suggesting that such metal fragments cannot fulfil ignition requirements. We found a spatial relationship between forestry machine ignitions and abundance of large stones, represented by a Boulder Index generated from a nationwide dataset. Further, 75% of the dispatches occurred on days with relative humidity <45%, Duff Moisture Code (Canadian system) >26 and Fire Weather Index >12. 75% of the area burned when Fire Weather Index was >20. Results suggest machine-caused forest fires can be largely avoided by cancelling operations in stony terrain during high-risk weather.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".