A satellite-based burned area dataset for the northern boreal region from 1982 to 2020
Bibliographic record
Abstract
Background Fires in the boreal forest occur with natural frequencies and patterns. Burned area (BA) is an essential variable in assessing the impact of climate change in boreal regions. Aims Spatial wildfire occurrence data since the 1950s are available for North America. However, there are no reliable data for Eurasia, mainly for Siberia, during the 1980s and 1990s. Methods A Bayesian-network algorithm was applied to the Long-Term Data Record (LTDR) Version 5 to generate a BA DataSet (BA-LTDR-DS) for the Boreal region from 1982 to 2020, validated using official reference data and compared with the MODIS MCD64A1 product. Key results A high correlation (>93%) with all the reference BA datasets was found. BA-LTDR-DS data grouped by decades estimated a linear increase in BA of 4.47 million ha/decade. This trend provides evidence of how global warming affects fire activity in these boreal forests. Conclusions BA-LTDR-DS constitutes a unique data source for the pre-MODIS era, and becomes a reliable source when other products with higher spatial/spectral resolution are not available. Implications The BA-LTDR-DS dataset constitutes the longest time series developed for the boreal region at this spatial resolution. BA-LTDR-DS could be used as input in global climate models, helping improve wildfire prediction capabilities and understand the interactions between fire, climate and vegetation dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".