Effect of Topography on Persistent Fire Refugia of the Canadian Rocky Mountains
Bibliographic record
Abstract
Persistent fire refugia, which are forest stands that have survived multiple fires, play an important ecological role in the resilience of mountainous forest ecosystems following disturbances. The loss of numerous refugia patches to large, high-severity fires in recent years is prompting the need to better understand drivers of fire refugia endurance. We investigate the role of topographic features on fire refugia survivorship based on pre-1950 fire regime conditions. Mapped refugia patches (n = 557) covering 28% of the forested landscape were used to develop three predictive models based on patch size (all sizes, <30 ha, <10 ha), as a function of explanatory variables describing several components of topography. Five topographic variables consistently favoured persistent fire refugia occurrence, though the ranking of explanatory variable importance varied among patch-size models. For the all-refugia model, elevation (23.7%), proportion of non-fuel at a 5000-m scale (20.3%), solar radiation (14.6%), Topographic Position Index at a 2000-m scale (10.1%), and distance from rivers (10.1%) were the top variables. The models’ predictive abilities were high, but decreased with larger patch sizes. We conclude that many suitable areas are currently unoccupied by fire refugia; that random elements affect their survivorship; and that additional environmental factors not considered in this study may contribute to their persistence. With changing climate and fire-regime conditions, careful fire and forest management considerations will be needed to limit future losses of persistent fire refugia 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".