Early Regeneration Dynamics of Pure Black Spruce and Aspen Forests after Wildfire in Boreal Alberta, Canada
Bibliographic record
Abstract
Research Highlights: Black spruce (Picea mariana Mill.) and trembling aspen (Populus tremuloides Michx.) both regenerated vigorously after wildfire. However, pure semi-upland black spruce stands are at increasing risk of changing successional trajectories, due to greater aspen recruitment. Background and Objectives: Black spruce and aspen are found across the boreal forest with black spruce dominating lowlands and aspen being common in uplands. Both species are well adapted to wildfire with black spruce holding an aerial seedbank while aspen reproduce rapidly via root suckering. In the summer of 2016, the Horse River wildfire burned 589,617 hectares of northern Alberta’s boreal forest. Methods: We assessed early regeneration dynamics of both pure aspen and pure black spruce forests. For black spruce, 12 plots were established in both bog and semi-upland habitats to assess seedling regeneration and seedbed availability. For aspen, 12 plots were established in each of the low, moderate, and high burn severities, as well as 5 unburned plots. Results: Post-fire black spruce regeneration densities did not differ between bog and semi-upland habitats, but were positively correlated with forb cover and charred organic matter seedbeds. Aspen regeneration within pure black sprue stands was substantial, particularly in semi-upland habitats, indicating a potential shift in successional trajectory. Fire severity did not significantly affect aspen regeneration in pure aspen stands, but regeneration density in all severity types was >90,000 stems ha−1. Aspen regeneration densities were negatively related to post-fire forb and shrub cover, likely due to competition and cooler soil temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".