Spatial and temporal patterns of wildfire ignitions in Canada from 1980 to 2006
Bibliographic record
Abstract
A spatially explicit baseline measure of historic, current and future wildfire ignition expectations is required to monitor and understand changes in fire occurrence, the distribution of which climate change is anticipated to modify. Using spatial–temporal patterns of fire in Canada, we present a method to identify baseline expectations and ignition trends between 1980 and 2006 across 1-km spatial units. Kernel density estimates of wildfire ignitions and temporal trajectory metrics were calculated to describe expected ignition density, variability from expected density, and increasing or decreasing density trends. Baseline ignition expectations and trends were used to create unique fire ignition regimes and assess anthropogenic influence on ignitions. Fire ignition densities decreased exponentially as distance to road or populated place increased, and largest ignition trends occurred closest to both variables. Fire ignition regime delineation was more dependent on human transportation networks than human settlement. These findings provide a unique approach to quantifying ignition expectations. This research highlights the potential of this baseline approach for monitoring efforts and fire–environment interaction research and offers a preliminary spatially explicit model of wildfire occurrence expectations in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".