Early vegetation control for the regeneration of a single-cohort, intimate mixture of white spruce and trembling aspen on upland boreal sites
Bibliographic record
Abstract
In Canada’s boreal forest region, there is increasing demand for practical regeneration strategies that will recreate mixed stands of white spruce ( Picea glauca (Moench) Voss) and trembling aspen ( Populus tremuloides Michx.). In 2002, we implemented an experiment in both west-central Alberta and northeastern Ontario to better understand the effects of herbaceous and woody vegetation control on crop tree survival and growth, within the context of prescription development for the regeneration of a single-cohort, intimate mixture of spruce and aspen. After five growing seasons, good spruce growth, health, and survival were observed with 2 m radial treatments consisting of herbaceous and woody (i.e., complete) vegetation control centred on trees planted at 5 m spacing. These spruce were 4%–64% taller and 68%–178% larger in stem diameter than untended trees, leading to 167%–1166% gains in stem volume, and were at least equivalent to the same stock grown at 2.5 m spacing and provided with complete, continuous relief from competition. Removing only the woody vegetation within treated radii stimulated herbaceous competition, resulting in reduced survival and growth of spruce and reduced height of surrounding aspen. Early results suggest that spot treatments that provide 2–4 years of relief from herbaceous and woody competition may offer a practical strategy for growing spruce with aspen.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".