A 400-year history of fires on lake islands in south-east Sweden
Bibliographic record
Abstract
Island-lake ecosystems are suitable for testing scale dependence in forests disturbance theories thanks to differences in the potential for fire spread on islands and the mainland. We investigated past fire regime on the mainland and on islands in a large lake in south-east Sweden. We used dendrochronological methods to reconstruct fire disturbances on 18 small islands (0.04–24.1 ha) and in 43 sites in the surrounding 75-km2 landscape over the last 400 years. In the past, fires were frequent on both islands and mainland but not synchronised on an annual scale. Significant temporal changes occurred around the middle of the 18th century. Before 1750, fires were less frequent on islands than on the mainland (median fire return interval 58 v. 25 years respectively). However, an inversion of this pattern was observed during 1750–1860: islands showed even shorter fire intervals than mainland locations, suggesting additional and likely human-related source of ignitions (median fire return interval 15 v. 29 years respectively). A substantial decrease in fire activity in both islands and mainland was apparent in 1860–1890. We suggest that the present fire regime (the last 100 years) on the small islands is largely natural as fire suppression is not present there. The dynamic nature of the fire regime on islands still requires further studies: islands may, at times, attract lightning, humans with fire, or both.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".