Growing Space Management in Boreal Mixedwood Forests: 22 Year Results
Bibliographic record
Abstract
Boreal mixed forests of trembling aspen (Populus tremuloides) and white spruce (Picea glauca) can provide higher outputs of many ecosystem goods and services, combined with resilience for changing environmental conditions. In this study, we examine the growth of white spruce and aspen over a range of stand compositions created by spot and broadcast treatments of broadleaves using manual and chemical means, aspen spacing, and untreated control. Twenty-two growing seasons postharvest, treatment responses created a range of broadleaf densities and spatial arrangements, reflected in significant differences in heights and diameters of the spruce mixedwood component. At this early seral stage, treatments have generated diversity in both tree species composition and stand structure. Modeling of the stand structures created by the different treatments provided outcomes ranging from pure conifer to broadleaf dominated when assessed by merchantable volumes. Broadleaf manipulations changed the relative proportion of spruce and broadleaf species in a mixture, achieving a variety of potential landscape and stand-level goals. Treatment differences were often not statistically significant until at least 16 years after stand initiation. Monitoring the achievement of mixedwood management goals will require longer time periods than currently employed for broadleaf or conifer monocultures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".