The influence of partial harvesting and forest floor disturbance on nutrient availability and understory vegetation in boreal mixedwoods
Bibliographic record
Abstract
The impacts of partial cut systems on nutrient availability and understory vegetation are poorly understood. To examine these responses, white spruce dominated stands in the boreal mixedwood of Alberta were clear-cut or partial-cut and the forest floor treated by slash burning, mixing, mounding, or scalping in a split-plot design. Soil nutrient availability (ion exchange resin), net N mineralization (in situ incubations), and vegetation (density and cover) responses were assessed. With the exception of higher Mg availability in the clearcuts, differences in nutrient availability were driven by forest floor disturbance and not harvest method. Relative to controls, burning increased availability of NH4+, NO3, and P, and scalping increased Ca and Mg but diminished K. Controls had low levels of NO3. The mixing treatment substantially reduced net N mineralization. In terms of vegetation, partial cuts reduced root suckering by Populus spp. (Populus tremuloides Michx., Populus balsamifera L.) relative to clearcuts. Burning and mounding stimulated fireweed (Epilobium angustifolium L.) cover, while scalping increased Populus spp. sucker density. In contrast, mixing largely reduced vegetation establishment, likely because of the destruction of roots and rhizomes and reduced N supply. Nutrient availability and vegetation establishment were more strongly controlled by forest floor disturbance than by partial canopy retention.
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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 | 0.000 |
| 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 | 0.001 | 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".