Bibliographic record
Abstract
This paper developes statistical models of the size distribution of lightning-caused wildfires in the boreal mixedwood forests of Alberta, Canada, for the intervals 1980–1998 and 1961–1998. Above any minimum threshold size [Formula: see text]3 ha, the logarithm of fire size is approximately exponentially distributed. However, computer simulations using the best-fit distribution would over predict the frequency of large fires, and thus the mean rate of disturbance. A truncated exponential distribution, which places an upper bound on fire size, is more suitable and, according to probability plots, provides an excellent fit to the data. I estimate the maximum fire size in the study area to be [Formula: see text] 650 000 ha. This estimate is insensitive to the choice of lower bound for fire sizes (between 3 and 1000 ha) and to the choice of sampling interval. Parametric modelling of fire sizes using covariates derived from forest inventory data shows that the expected size of a fire is positively related to the abundance of pine forest in the vicinity of the point of detection and negatively related to the abundance of recently logged or burnt areas. This implies that variation in forest structure and disturbance history impose marked spatial variability on the fire size distribution. Other covariates, such as periodic indices of fire weather, could readily be evaluated in this framework.
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 | 0.002 | 0.002 |
| 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".