Initial response of understorey vegetation to fire severity and salvage‐logging in the southern boreal forest of Québec
Bibliographic record
Abstract
Abstract. In this study we compared the effects of fire on understorey vegetation in the Québec southern boreal forest with effects of salvage‐logging (clear‐cutting after fire). All 61 400‐m2 sampling sites were controlled for overstorey composition (Deciduous, Mixed and Coniferous) and disturbance type, which consisted of three fire impact severity (FIS) classes (Light, Moderate and Extreme) and two harvesting techniques (Stem‐only and Whole‐tree Harvesting). Percent‐cover data of vegetation and post‐disturbance environmental characteristics were recorded in the field during the first two years after fire as well as soil texture. Ordination of fire alone demonstrated that, on Coniferous sites, fire initiates a succession whereby the understorey Coniferous sites approaches that of Deciduous‐Mixed sites, due to the release of the understorey from Sphagnum spp. dominance, this pattern being a function of FIS. On Deciduous‐Mixed stands, increased FIS resulted in a transition from herb to shrub dominance. Ordination of all five disturbance types showed that the impact of salvage‐logging on understorey composition was within the range of fire, but marginalized to the extreme end of the FIS spectrum. Variance partitioning demonstrated that overstorey and soil texture were the most important explanatory variables of fire alone, while disturbance type explained the largest independent fraction of understorey variation when salvage‐logging was introduced. Salvage‐logging also results in significant reductions in understorey abundance, richness and diversity, while indicator species analysis suggests that it favours mesoxerophytic to xeric species. Results are interpreted in light of shade‐tolerance dynamics, forest floor disturbance and soil moisture regimes. Implications for sustainable forest management are discussed.
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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) | 0.002 | 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".