A long-term satellite-based burned area database for the Northern Boreal Region (1982-2020)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it