Prescribed burning of harvested boreal black spruce forests in eastern Canada: effect on understory vegetation
Bibliographic record
Abstract
Ecosystem-based management advocates that forestry disturbances should aim to emulate natural disturbances to mitigate the landscape-level impact of forest management. This study compares the impact of clear-cuts followed by a prescribed burn (CCPB) with clear-cuts alone (CC) and current careful logging practices (CLAAG: “careful logging around advanced growth”) on understory composition within black spruce (Picea mariana Miller (BSP)) paludified forest stands at the plot, site, and treatment levels using a functional-type approach. Vascular and nonvascular taxa showed significant differences in composition at the plot level among treatments. We found that pioneer taxa occurred mainly in CCPB sites, while late-successional taxa characterized CC sites. CLAAG sites had higher taxa richness than CCPB and CC sites, and we found that CCPB treatments were most likely to promote vascular taxa compositions that are more similar to those observed after natural disturbances. Additionally, the relative abundance of Sphagnum spp., responsible for paludification, was significantly reduced in sites treated by prescribed burning. This study therefore presents results suggesting that prescribed burning might represent a sustainable alternative to current harvesting techniques in the Clay Belt of eastern Canada that could help in preserving biodiversity (in terms of understory species assemblage) while maintaining or even enhancing forest productivity.
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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 | 0.001 | 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 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".