Bibliographic record
Abstract
Wildfires have a significant impact on climate and ecosystems in Canada and Alaska. Characterizing fire regimes and projecting fire recurrence intervals for different biomes are important in managing those ecosystems and quantifying carbon dynamics of those ecosystems. Wildfires statistics for the conterminous Canada and Alaska are examined in a spatially and temporally explicit manner. The effort in this thesis used Canadian wildfire datasets, 1980–1999, to characterize relationships between number of fires and burned area for 13 ecozones, and to calculate wildfire recurrence intervals for each ecozone, using the parameters of the power-law frequency-area distributions. For the conterminous Canada, we find that: (a) Despite the many complexities concerning their initiation and propagation, wildfires exhibit power-law frequency-area statistics over many orders of magnitude in each ecozone and the whole of Canada; (b) The ratio of number of small to large fires generally increases from north to south of Canada; (c) Human ignition sources have higher probability to cause larger ratio of number of large to small fires; (d) Fire recurrence intervals ranged from 1 to 32 years for burned areas larger than 2 km 2, and from 1 to 100 years for burned areas larger than 10 km 2 in every 10,000 km2 spatial area for each ecozone. The results have a number of practical implications. First, the frequency-area distribution of small and medium fires can be used to quantify the risk of large fires. Second, the behavior of the forest fire model can be used to assess the role of controlled burns to reduce the hazard of very large fires. The findings of this study will also be a benefit to future efforts in quantifying carbon dynamics in Canadian boreal terrestrial ecosystems. Further effort should be put on the impact of fire disturbance on ecosystem structure and carbon dynamics. Interaction between fire and climate also needs much more attention, especially in a global warming period.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".