Managed Mixtures of Aspen and White Spruce 21 to 25 Years after Establishment
Bibliographic record
Abstract
Intimate mixtures of trembling aspen (Populus tremuloides Michx.) and white spruce (Picea glauca (Moench) Voss) are a key feature of western Canadian boreal forests. These mixtures have the potential to produce high yields of merchantable fibre and provide numerous ecological services. Achievement of this potential has been difficult, and often expensive, to realize as a regeneration goal in managed forests. We report 21 to 25 year results of managed mixtures on two study sites where the white spruce was planted, and the density of aspen natural regeneration manipulated within five years of the stand initiation disturbance. On both sites, white spruce mortality did not increase with increasing aspen density. While height and diameter growth of white spruce declined with increasing aspen density, the effect was not entirely consistent across the two sites. Abrasion from aspen branches was the most common source of damage to spruce crowns. Mixed stands had greater merchantable volume production than pure spruce stands based on model projections. Application of aspen harvest at year 60, while protecting the spruce component for a second harvest entry at year 90, was projected to optimize combined yield for the mixedwood stands.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".