Four National Maps of Broad Forest Type Provide Inconsistent Answers to the Question of What Burns in Canada
Bibliographic record
Abstract
Wildfires are burning increasingly extensive areas of forest in Canada, reducing their capacity as carbon sinks. Here we compare the answers that four independent land cover datasets, produced from different satellite images (SPOT, Landsat, and MODIS), provide for the question of what burned in Canada in recent years. We harmonized the different datasets into a common, simpler legend consisting of three classes of forest (needle-leaf, broadleaf, and mixed) plus non-forest, and resampled them to a common pixel size (250 m). Then we used annual maps of burned area to count, for each map and year from 2011 to 2014, the number of burned pixels of each class, and we tabulated them by terrestrial ecozone and Canada-wide. While all four maps agree that needle-leaf forest is the most frequently burned class in Canada, there is great disparity in the results from each map regarding the proportion of burned area that each class represents. Proportions reported by one map can be more than double those reported by another map, and more than four times at the ecozone level. We discuss the various factors that can explain the observed discrepancies and conclude that none of the maps provides a sufficiently accurate answer for applications such as carbon accounting. There is a need for better information in areas lacking forest inventory, especially in the vast unmanaged forest of Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".