Modeling risks of climate-driven wildfires in boreal forest: the FLAM approach
Bibliographic record
Abstract
Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA,and are now an increasing threat in boreal Europe. We will present approaches to modeling wildfire dynamicsusing the wildFire cLimate impacts and Adaptation Model (FLAM) being developed at the International Instituteof Applied Systems Analysis (IIASA). FLAM operates on a daily time step and uses mechanistic algorithms toparametrize the impacts of climate, human activities, and fuel availability on wildfire probabilities, frequencies,and burned areas. Model validation on historical GIS and remote sensing data and future projections underclimate change scenarios will be discussed at various scales and resolutions for the boreal forest. We willpresent modeling results for the boreal forest, including: (i) simulation of burned areas and adaptation options;(ii) projections of burned areas driven by climate change scenarios until 2100; (iii) regional variability and drivingforces behind forest fires in Sweden. Our results support international analyses that, irrespective of changes inmanagement, it is evident that climate change is very likely to increase the frequency and impact of wildlandfires in the coming decades, also in the boreal forest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".