Large forest fires in Canada, 1959–1997
Bibliographic record
Abstract
A Large Fire Database (LFDB), which includes information on fire location, start date, final size, cause, and suppression action, has been developed for all fires larger than 200 ha in area for Canada for the 1959–1997 period. The LFDB represents only 3.1% of the total number of Canadian fires during this period, the remaining 96.9% of fires being suppressed while <200 ha in size, yet accounts for ∼97% of the total area burned, allowing a spatial and temporal analysis of recent Canadian landscape‐scale fire impacts. On average ∼2 million ha burned annually in these large fires, although more than 7 million ha burned in some years. Ecozones in the boreal and taiga regions experienced the greatest areas burned, with an average of 0.7% of the forested land burning annually. Lightning fires predominate in northern Canada, accounting for 80% of the total LFDB area burned. Large fires, although small in number, contribute substantially to area burned, most particularly in the boreal and taiga regions. The Canadian fire season runs from late April through August, with most of the area burned occurring in June and July due primarily to lightning fire activity in northern Canada. Close to 50% of the area burned in Canada is the result of fires that are not actioned due to their remote location, low values‐at‐risk, and efforts to accommodate the natural role of fire in these ecosystems. The LFDB is updated annually and is being expanded back in time to permit a more thorough analysis of long‐term trends in Canadian fire activity.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".