Spatial distribution of mean fire size and occurrence in eastern Canada: influence of climate, physical environment and lightning strike density
Bibliographic record
Abstract
In Canada, recent catastrophic wildfire events raised concern from governments and communities. As climate change is expected to increase fire activity in boreal forests, the need for a better understanding of fire regimes is becoming urgent. This study addresses the 1972–2015 spatial distributions of fire cycles, mean fire size (FireSz) and mean fire occurrence (mean annual number of fires per 100 000 ha, FireOcc) in eastern Canada. The objectives were to determine (1) the spatial variability of fire-regime attributes, (2) the capacity of FireSz and FireOcc to distinguish homogeneous fire zones and (3) the environmental factors driving FireSz and FireOcc, with some emphasis on lightning strikes. Fire cycles, FireSz and FireOcc greatly varied throughout the study area. Even within homogeneous fire zones, FireSz and FireOcc were highly variable. FireSz was controlled by moisture content in deep layers of the soil and by surficial deposits, whereas FireOcc was controlled by moisture content in top layers of the soil and by relief. The lack of a relationship between FireOcc and lightning-strike density suggested that the limiting effect of lightning-strike density on FireOcc could be operating only under certain circumstances, when interacting with other environmental factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".