The influence of wildfire boundary delineation on our understanding of burning patterns in the Alberta foothills<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.
Bibliographic record
Abstract
Under the auspices of ecosystem-based management, historical disturbance patterns are promoted as a means of providing benchmarks for ecosystem sustainability. The associated research in support of this strategy in the boreal forest has substantially increased our understanding of frequencies, sizes, shapes, and severities of wildfires. However, despite the fact that different spatial definitions of wildfires exist in both research and practice, we have not considered the significance or impact of those differences on observed patterns. This study addresses this gap by conducting a sensitivity analysis on the influence of 11 spatial definitions of a wildfire on six pattern metrics for 24 wildfires in the Foothills Natural Region of Alberta. The results suggest that all pattern metrics were sensitive to changes to wildfire delineation, but in particular the total amount of remnants, wildfire shape, and the relationship between pre-burn fuel types and the probability of burning. The results also suggest that simple mortality maps do not necessarily identify multiple disturbed patches within wildfires, an attribute undocumented by previous research. These pattern differences potentially correspond to some fundamental differences in perception of how and why wildfires burn and our understanding of the associated processes and biological responses.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".