Fire history and climate-fire relations in Jasper National Park, Alberta, Canada
Bibliographic record
Abstract
In mixed-conifer forests of western North America, fire ecologists and managers are increasingly recognizing the prevalence and importance of mixed-severity fire regimes. However, these fire regimes remain poorly understood compared to those of high- and low-severity. To enhance understanding of fire regimes in the montane forest of Jasper National Park (JNP), I reconstructed fire history and assessed forest composition, age and size structure at 29 sites (Chapter 2). Historic fires were of mixed severity through time at 18 sites, whereas the remaining 11 sites had evidence of high-severity fires only. At the site level, mean importance values of canopy trees were more even among coniferous species and greater for Pseudotsuga menziesii at mixed-severity sites. The greater numbers of veteran trees and discontinuous age structures were also significant indicators of mixed-severity fire histories. In a second study, I crossdated tree ages and fire-scar dates for 172 sites and tested whether historic fire occurrence depended on inter-annual to multi-decadal variation in climate (Chapter 3). Eighteen fires between 1646 and 1915 burned during drought years, with a weak association to El Niño phases and the negative phase of the Pacific Decadal Oscillation. Fire frequency varied through time, consistent with climate drivers and changes in land use at continental to inter-hemispheric scales. No fire scars formed since 1915, although potential recorder trees were present at all sites and climate was conducive to fire over multiple years to decades. Thus, the absence of fires during the last century can largely be attributed to active fire suppression. Improved understanding of the drivers of the historic mixed-severity fire regime enhances scientifically-based restoration, conservation, forest and wildfire management in the Park and surrounding montane forests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".