A long-term satellite-based burned area database for the Northern Boreal Region (1982-2020)
Bibliographic record
Abstract
Burned Area (BA) is an essential variable to study the Earth’s climate evolution. The boreal forest is one of the largest biomes in the world, spanning North America and Eurasia. For North America, two record fire occurrence databases since the 1950s are available: Alaska Fire Service (AFS) database and the Canadian National Fire Database (CNFDB). However, there are currently no reliable burned area data for the boreal region of Eurasia, mainly Siberia, for the 1980s and 1990s. This work describes the application and technical validation of a Bayesian network algorithm to the Long-Term Data Record version 5, to generate a burned area product at a resolution of 0.05 degrees for the entire boreal region above 60°N from 1982 to 2020. The burned area estimates have been evaluated using high-resolution satellite images, official reference data and the MCD64A1 MODIS global burned area product (when were available). The results show a high correlation with all the reference burned area datasets (95% with AFS-CNFDB, 93% and 95% with MCD64A1 in North America and Eurasia, respectively). The derived database constitutes a unique long-term burned area information for studies of fire and carbon dynamics in the Northern Boreal Region, as well as their effects on the climate system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 0.001 |
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".