Canadian wildfires: a spatial-temporal assessment of fire activity and cause (1988 to 2018)
Bibliographic record
Abstract
Vegetation zones such as the boreal forest in Canada have been shaped and maintained by naturally occurring wildfires for centuries. However, as global climates have warmed due to an increase in greenhouse gases within the atmosphere, there has been a profound impact on Canada’s forests. As fire activity continues to be very influential in altering forest biomes in Canada, it is important to analyze and evaluate these changes. The focus of this study is on assessing change in fire frequency, severity, and cause of fire disturbances in relation to where people reside in Canada. The timeframe for this study is a 30-year span, 1988 to 2018. The datasets utilized allowed for both temporal and spatial analysis of forest fires for each province and territory in Canada. Datasets were analyzed, and maps were developed using ESRI’s ArcMap GIS software. There has been an increase in both frequency and severity (in terms of area size) of forest fires over these 30 years. The main cause of this upsurge in fire activity is associated with lightning, but human accident fires have also steadily increased particular in proximity to Canada’s ecumene (where most people live). Human prescribed fires have also surged, especially in western Canada, as these deliberately set fires have become more necessary in efforts to safeguard Canada’s forest resource and vulnerable populations. As the geography of forest fire activity continues to evolve in Canada, this type of spatial-temporal research is useful to those who develop new policies, mitigation plans, and adaptation strategies to protect the vitality of forest ecosystems and the safety of Canadian populations
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".