Influence of landscape structure on patterns of forest fires in boreal forest landscapes in Sweden
Bibliographic record
Abstract
To analyze the effect of landscape structure (viz. amount of wetlands) on the past forest fire regime in boreal Sweden, we reconstructed detailed fire histories by cross-dating fire scars in living and dead Scots pine (Pinus sylvestris L.) in two different landscape types: mire-free landscapes with a low proportion (1%2%) of mires and mire-rich landscapes with a high proportion (21%33%) of mires. Two localities were selected and at each one, adjacent mire-free and mire-rich areas of 256601 ha were sampled. Over the studied 650-year period, the two landscape types differed primarily in the fire intervals and sizes of fires. In the mire-rich landscapes, fires had frequently stopped against mire elements. The net effect was significantly longer fire intervals in the mire-rich than in the mire-free landscape (on average, 32 versus 56 years). The mire-rich areas also had a tail of very long fire intervals lacking in the mire-free areas (maximal interval 292 years). We conclude that mires can have a profound effect on both spatial and temporal patterns of forest fires in the boreal forest, but only when they are effective fuel breaks (i.e., they are wet enough) at the time the fires burn and if they truly dissect the nonmire portion of the forest landscape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".