Stand-level effects of soil burn severity on postfire regeneration in a recently burned black spruce forest
Bibliographic record
Abstract
This study tested whether variations in soil burn severity (soil organic layer consumption) influenced patterns of early postfire plant regeneration in a black spruce (Picea mariana (Mill.) BSP) forest in interior Alaska. Variations in burn severity were related to measurements of postfire tree seedling establishment and cover of plant growth forms observed 78 years after fire. Black spruce and trembling aspen (Populus tremuloides Michx.) showed significant and opposite responses of seedling density to changes in soil burn severity. Positive correlations between burn severity and aspen density and individual seedling biomass led to an increase of over three orders of magnitude in aspen standing biomass (aboveground, g/m2) from the least to most severely burned sites. Variations in aspen productivity and consequent effects on litter production and seedbed quality possibly explain the observed negative response of black spruce density to increasing burn severity. Variations in the cover of several plant growth forms were also strongly correlated with patterns of soil burn severity. Regenerating plant communities in low-severity sites had a greater cover of evergreen shrubs and graminoids, while high-severity sites had increased cover of aspen and acro carp ous mosses. Observations of regeneration patterns in the burn are largely consistent with experimental studies of severity effects and suggest that variations in soil burn severity can have a strong influence on landscape patterns of postfire forest recovery. In this case, increases in burn severity have shifted successional trajectories away from simple conifer self-replacement towards a trajectory of mixed conifer and deciduous dominance.
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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 | 0.000 | 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) | 0.001 | 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".