Wildfires in boreal ecosystems: past, present and some emerging trends
Bibliographic record
Abstract
With the emergence of a new forest management paradigm based on the emulation of natural disturbance regimes, interest in fire-related studies has increased in the boreal forest management community. A key issue in this regard is the improvement of our understanding of the variability in past disturbances and its linkages with climate and ecosystems. The surge in research activity has further been exacerbated by the increasing awareness of climate change, which has already exposed boreal forests to greater fire risk in recent decades. It is anticipated that further warming and drying will further enhance fire frequency and area burned in many boreal forests. Better predictions of future fire activity will contribute to better long-term forest planning in managed boreal forests. The 12 papers presented in this special issue exemplify this increased research activity by bringing together studies from diverse disciplines and presenting the latest advances regarding methodological approaches for reconstruction and modelling of past, present and future fire activity. Here we aim to summarise, evaluate and set into context some of the new insights arising from these studies and also to discuss some considerations to be taken into account in future research activities.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".