High-resolution modeling of historical forest fires in the Canton of Bern
Bibliographic record
Abstract
Forest fires are considered an important hazard in forested areas and a serious threat to forest ecosystem and buildings. The combination of drought, high temperatures, and wind increases the risk of forest fires. To better understand the fundamental causes and consequences of fire, we need to study the historical fire regimes. In this study, the meteorological conditions were simulated with the WRF model (Weather Research and Forecasting; Skamarock et al. 2008) for three historical forest fires, in the Canton of Bern, Switzerland (La Neuveville, April 1893, Simmenflueh, August 1911, Kirchberg, April 1915). In terms of area, these are the largest fires in the canton of Bern in the Swiss fire database. The "Twentieth Century Reanalysis" version 3 (20CRv3, Slivinski et al. 2019) was used as a boundary condition. 20CRv3 has a spatial resolution of about 75 km and a temporal resolution of three hours. Using WRF version 4.1.2 20CRv3 has now been gradually downscaled to a resolution of 1x1 km^2. Simulations suggest that the soil had dried out in the previous week and soil moisture had reached low values on the day the fire broke out. High-resolution fire weather indices are also calculated. A lack of precipitation and high temperatures led to high forest fire index values and a high to very high risk of forest fires. References[1] Slivinski, L. C.et al. (2019), Towards a more reliable historical reanalysis: Improvements to theTwentieth Century Reanalysis system. , Q. J. Roy. Meteorol. Soc. 145, 2876-2908.[2] Pfister, L. , S. Brönnimann, M. Schwander , FA Isotta , P. Horton, and C. Rohr, (2020) StatisticalReconstruction of Daily Precipitation and Temperature Fields in Switzerland back to 1864, Clim.past 16, 663-678.[3] Skamarock, WC, et al. (2008) A Description of The Advanced Research WRF Version 3. NCARTechnical Note.[4] Van Wagner, C.E. (1987): Development and Structure of the Canadian Forest Fire Weather IndexSystem, Forestry Technical Report, Canadian Forestry Service Headquarters, Ottawa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".