Influence of forest composition on understory cover in boreal mixedwood forests of western Quebec
Bibliographic record
Abstract
Forest overstory composition influences both light and nutrient availability in the mixed boreal forest. The influence of stand composition on understory cover and biomass was investigated on two soil types (clay and till deposits). Four forest composition types were considered in this study: aspen (Populus tremuloides Michx.), paper birch (Betula papyrifera Marsh.), jack pine (Pinus banksiana Lamb.) and a mixture of balsam-fir (Abies balsamea (L.) Mill.) and white spruce (Picea glauca (Moench) Voss). The cover of all understory species was recorded while the biomass of two important and ubiquitous species was measured: mountain maple (Acer spicatum Lam.) of the shrub layer and large-leaved aster (Aster macrophyllus L.) of the herb layer. Soil analyses were conducted to evaluate the influence of overstory composition on understory biomass through its influences on soil characteristics. Analyses of variance showed a significant effect of forest canopy type on mountain maple biomass, understory cover and shrub cover but not on herb cover and large-leaved aster biomass. Path analysis was performed to explore the relationships between canopy type, nutrient availability and understory biomass. Contrary to what was expected, the variation in plant biomass associated with forest composition was weakly related to soil nutrient availability and more strongly related to stand structural attributes.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".