The importance of forest floor disturbance in the early regeneration patterns of the boreal forest of western and central Quebec: a wildfire versus logging comparison
Bibliographic record
Abstract
The nonvascular and vascular plant composition of the early regenerating vegetation present following wildfires and clear-cut logging has been compared separately in three areas of the black spruce (Picea mariana (Mill.) BSP) - feathermoss (Pleurozium schreberi (Brid.) Mitt.)) forest of western and central Quebec. In each area, a detrended correspondence analysis successfully differentiated the burned and logged stands along the first ordination axis. This separation mainly resulted from the greater abundance of pioneer species or lichens after fire and the greater abundance of residual species after clear-cutting. Spearman's correlation coefficients were calculated to relate variables characterizing physical disturbance of the forest floor and general site conditions to the two first differentiating axes. Variables characterizing forest floor disturbance severity were strongly associated with the first ordination axis in two of the study areas but not in the third one. The interpretation of compositional differences in the light of plant reproductive strategies led to the identification of regeneration patterns that illustrated the influence of disturbance type and severity on post-disturbance vegetation composition. These results suggest that certain forestry practices such as careful logging with the protection of regeneration and soil, scarification, and prescribed burning may differ in their capability to address sustainable forest management issues.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".