Modeling risks of climate-driven wildfires in boreal forest: the FLAM approach
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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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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 it