Fire Dynamics in Boreal Forests Over the 20th Century: A Data-Model Comparison
Bibliographic record
Abstract
Fire regimes across the world are expected to be altered by continuing variations in socio-economic conditions and climate. Current global fire-vegetation models are able to represent the present-day fire activity, but it is unclear how well they can simulate past or future scenarios. Here we use sedimentary charcoal-based biomass burning reconstructions to evaluate fire probability and total carbon flux emitted to the atmosphere per year simulated by the dynamic global vegetation model LPJ-GUESS with its incorporated fire model SIMFIRE-BLAZE across the boreal region during the last century. The analyses were run for the whole time period (1900–2000 CE), as well as for the intervals 1900–1950 CE and 1950–2000 CE. The data–model comparison for the 20 th century reveals a general disagreement in trends between charcoal reconstructions (with decreasing or stable trends) and simulations (showing an overall increase) at both global (boreal forests) and continental scales (North America and Fennoscandia), as well as for most of the regional sub-areas (Canada, Norway and Sweden). The only exceptions are Alaska and Finland/Russia Karelia, where all the variables increase. Negative correlations between observations and model outputs are also recorded for the two different sub-periods, except for Alaska and North America during the time interval 1900–1950 CE, and Norway and Finland/Russia Karelia between 1950 and 2000 CE. Despite several uncertainties in charcoal records, main differences between modeled and observed fire activity are probably due to limitations in the representation of the human impact on fire regime (especially connected to forest management and landscape fragmentation) in the model simulations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".