Forest Type and Wildfire in the Alberta Boreal Mixedwood: What Do Fires Burn?
Bibliographic record
Abstract
Two determinants of fire behavior are fire weather and spatial variation in fuels. Their relative importance in boreal forests has been unclear. I evaluated the effect of fuels on a ∼74 000-km2 landscape in the boreal mixedwood region of western Canada. My data were the compositions, or the proportional areas of different forest types, of 48 mapped lightning fires and of their immediate surroundings. I measured areal compositions from forest inventory maps, using a five-way classification representing deciduous forest, three types of coniferous forest, and wetlands. The fires burned between 1980 and 1993. Fire sizes ranged from 70 ha to 70 000 ha. By multivariate linear regression, fire surroundings explain 57% of the variation in forest types within mapped fires. Fire compositions are not representative of the study area as a whole, or of a fire's surroundings, and are unrelated to fire size and location within the study area. Using the model, I predicted the areas of the five types burned within all other lightning fires >200 ha in the study area during 1961–1996 and estimated type-specific mean annual burn rates. These rates vary by an order of magnitude. Deciduous stands burn at the lowest rate, and black spruce stands burn at the highest rate. Fires exhibit significant preferences between forest types at both local and regional scales. Preference orderings are similar at both scales and are generally consistent with the rank order of estimated burn rates. Preferential burning may result from between-class differences in vertical canopy structure and foliage characteristics. The statistical model and the postulated variations in fire behavior between classes indicate that landscape-scale fuels management may be feasible in this system. The rank ordering of burning frequencies and preferences is the inverse of the planned disturbance rates under forest management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".