Effects of Wildfire Severity on Early Successional Dynamics in Boreal Peatland Complexes
Bibliographic record
Abstract
The Arctic-boreal region is experiencing changes in climate, trending toward warmer summers, resulting in a greater occurrence of wildfires with longer burning periods and higher intensities. Drought-like conditions dry surface fuels, leading to a higher probability of ignition, even in lowland peatlands. Previous work has been done to characterize post-fire succession rates in boreal upland sites, but much less is known of fire effects and early successional dynamics in lowland peatlands. Areas surrounding the Great Slave Lake in Canada’s Northwest Territories experienced exceptional wildfire activity in 2014 and 2015. These fires burned a variety of ecotypes, including bogs, fens, other lowlands, and uplands. To relate fire severity to early succession following wildfires, we collected seedling regeneration data in 2015 and 2016 and used mixed modeling and multivariate analyses to relate patterns in post-fire succession to burn severity metrics. Our study quantified burn severity at the surface, shrub, and canopy layers at several burned sites across ecotypes. We found that the most significant indicator of early regeneration of coniferous trees were severe ground fires, with canopy severity having little influence on successional patterns. Patterns of early succession of deciduous trees, however, related more to canopy severity. This work adds much needed context for post-fire succession in boreal peatland ecosystems, as the susceptibility of these systems to burning will continue to increase with a warming climate.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".