Generating annual estimates of forest fire disturbance in Canada: the National Burned Area Composite
Bibliographic record
Abstract
Determining burned area in Canada across fire management agencies is challenging because of different mapping scales and methods. The inconsistent removal of unburned islands and water features from within burned polygon perimeters further complicates the problem. To improve the determination of burned area, the Canada Centre for Mapping and Earth Observation and the Canadian Forest Service developed the National Burned Area Composite (NBAC). The primary data sources for this tool are an automated system to derive fire polygons from 30-m Landsat imagery (Multi-Acquisition Fire Mapping System) and high-quality agency polygons delineated from imagery with spatial resolution ≤30 m. For fires not mapped by these sources, the Hotspot and Normalized Difference Vegetation Index Differencing Synergy method was used with 250–1000-m satellite data. From 2004 to 2016, the National Burned Area Composite reported an average of 2.26 Mha burned annually, with considerable interannual variability. Independent assessment of Multi-Acquisition Fire Mapping System polygons achieved an average accuracy of 96% relative to burned-area data with high spatial resolution. Confidence intervals for national area burned statistics averaged ±4.3%, suggesting that NBAC contributes relatively little uncertainty to current estimates of the carbon balance of Canada’s forests.
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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".