Long fire cycle in northern boreal Pinus forests in Finnish Lapland
Bibliographic record
Abstract
Knowledge of past forest fire regimes is important for developing management plans for conservation areas and for predicting the probable effects of forest management and climate change on the structure and dynamics of forests. In this study, fire scars on living and dead trees were systematically sampled on 256 study plots in three landscapes in northeastern Finland dominated by Scots pine ( Pinus sylvestris L.). A total of 1030 disks or partial cross sections from different trees, including scars from 98 distinct forest fires, were dendrochronologically dated with an accuracy of 1 year or better. The extraordinarily well-preserved old Pinus snags and stumps allowed us to reconstruct annual tree-ring and fire chronologies beginning from the year AD 653. The fire cycles in the studied landscapes were exceptionally long for a boreal region dominated by Pinus, on average 350 years during the last millennium. This demonstrates that the fire regimes of poorly studied remote regions cannot be extrapolated from fire regimes of sites examined in more detail. Based on statistics on lightning-ignition densities, we suggest that most of the fires detected in this study were ignited by humans. The reconstructed past fire cycles were probably shortened by human influence.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".