Area burned adjustments to historical wildland fires in Canada
Bibliographic record
Abstract
Abstract Prior to delineation of fire perimeters from airborne and satellite imagery, fire management agencies in Canada employed conventional methods to map area burned based on sketch mapping, digitization from a global positioning system unit, and point buffering from geographic coordinates. These techniques usually provide a less precise representation of a wildland fire’s size and shape than those derived from image data. The aim of this study is to assess the discrepancy in fire size from these techniques that contribute to uncertainty in area burned. We paired independently generated fire perimeters derived from Landsat satellite imagery with conventional perimeters (n = 2792; mean area difference per fire = 40.1%), and developed a set of prediction models to estimate a Landsat area burned from conventional perimeters by considering the mapping source, method, agency, and time period. A two-fold cross validation predicting the logarithm of area burned from the models, indicated an R 2 = 0.95 (MAE = 0.10 ha; RMSE = 0.19 ha). From this, we created an adjusted area burned time series from 1950 to 2018 using the model-predicted estimates from conventional perimeters (75% of agency-reported area) in combination with unchanged estimates from agency perimeters derived from airborne and satellite imagery (13% of fires). The predicted estimates reduced the size of individual fires over 2000 ha on average in some years, contributing to an annual average reduction of approximately 11% of the area burned reported in the national agency fire database. By retrospectively applying a robust statistical adjustment to the fire size data, the historical overestimation in annual area burned—up to 1.4 Mha in a single year—could be substantially minimized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".