Altered mixed-severity fire regime has homogenised montane forests of Jasper National Park
Bibliographic record
Abstract
Fire suppression has altered the historical mixed-severity fire regime and homogenised forest structures in Jasper National Park, Canada. We used dendrochronology to reconstruct fire history and assess forest dynamics at 29 sites in the montane forests. Based on fire scars and even-aged post-fire cohorts, we determined 18 sites had mixed-severity fire histories through time, and 11 sites had evidence of high-severity fires only – yielding a mixed-severity fire regime for the study area. Lodgepole pine, hybrid spruce and Douglas-fir established simultaneously after low- and high-severity fires. Regardless of fire history, forest canopies were mixed in composition and subcanopies were strongly dominated by shade-tolerant hybrid spruce. Despite their size, subcanopy trees were similar in age to the canopy trees. Current stand composition and age structures largely reflect the effects of high-severity fires that burned ~110 years ago at 18 of 29 sites. In the absence of fires after 1905, forests have matured simultaneously, homogenising the landscape and resulting in forest structures that are more conducive to high-severity fire than are fires of a range of severities. Proactive fire management is justified to restore fire as a vital ecological process and promote forest resilience by countering the effects of a century of fire suppression.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".