Quantification of wildfires in North American permafrost domain, based on the MODIS Fire product
Bibliographic record
Abstract
A number of studies have shown that the boreal forest in North America has experienced a nearly two-fold increase in burned area over the past 60 years, with a corresponding rise in the number of large fires. Permafrost is the dominant ground cover for most of the boreal forest, and forested areas experience greater fire impacts that may decrease permafrost by up to 9-16% by 2100. Modern fire regimes rely on remotely sensed satellite data, including the active fire product from the Moderate Resolution Imaging Spectrometer (MODIS), which has been gathering data since 2000. This study uses the MODIS Active Fire product to quantify the area that experienced fires in permafrost areas, covering a study area between 50 and 70 degrees in North America divided into four subregions, over a 20-year period from 2001 to 2021. The results show that more than 75% of fires occur in central and western Canada, compared to 20% in Alaska and only 5% in Quebec and the Labrador Peninsula. Furthermore, the results indicate that fires occur in all types of permafrost, regardless of ground type, where Alaska has the highest number of fires occurring on permafrost ground, with 96% of all fires occurring in that region. Fires are highly influenced by climate as it determines the tree line of the boreal forest. Additional spatial distribution analyses support these findings. The study also examines trends in the spatial and temporal distribution of fires and finds that 20 years is a short period for analysis, given the significant variation in fire occurrence between years. Finally, the study concludes that while the active fire product has limitations, it is a valuable tool when used in conjunction with other sources of wildfire information such as the Burned Area product and Fire Radiative Power (FRP), as well as other wildfire databases prior to the year 2000.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".