Drivers of extreme wildfire years in the 1965–2019 fire regime of the Tłıchǫ First Nation territory, Canada
Bibliographic record
Abstract
Exceptionally large areas burned in 2014 in central Northwest Territories (Canada), leading members of the Tłı̨chǫ First Nation to characterize this year as ‘extreme’. Top-down climatic and bottom-up environmental drivers of fire behavior and areas burned in the boreal forest are relatively well understood, but not the drivers of extreme wildfire years (EWY). We investigated the temporal and spatial distributions of fire regime components (fire occurrence, size, cause, fire season length) on the Tłı̨chǫ territory from 1965 to 2019. We used BioSIM and data from weather stations to interpolate mean weather conditions, fuel moisture content and fire-weather indices for each fire season, and we described the environmental characteristics of burned areas. We identified and characterized EWY, i.e., years exceeding the 80th percentile of annual area burned for the study period. Temperature and fuel moisture were the main drivers of areas burned. Nine EWY occurred from 1965 to 2019, including 2014. Compared to non-EWY, EWY had significantly higher mean temperature (>14.7°C) and exceeded threshold values of Drought Code (>514), Initial Spread Index (>7), and Fire Weather Index (>19). Our results will help limit the effects of EWY on human safety, health and Indigenous livelihoods and lifestyles.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".