Conifer performance, stand productivity, and understory cover in varying densities of mixed conifer-broadleaf stands in southwestern British Columbia
Bibliographic record
Abstract
There is an increasing interest in mixed conifer-broadleaf stands as a way to increase the diversity and productivity of managed forests. This study examined the impacts of varying densities of planted broadleaf trees on conifer performance, total stand productivity, and understory plant cover 20 years after stand establishment. The study took place in the Malcolm Knapp Research Forest in Maple Ridge, British Columbia. It used a randomized complete block design to compare treatments containing low, moderate, or high broadleaf densities added to a constant conifer density. Each block contained a conifer-only plot as a control. Conifers were composed of equal amounts of western hemlock, western redcedar, and Douglas-fir. Broadleaves were composed of either red alder or paper birch. We found that conifer volume was significantly lower in most broadleaf treatments relative to the control, due to lower hemlock and redcedar volumes. Douglas-fir, on the other hand, had a higher volume—albeit not significant—in the broadleaf treatments. There were no significant differences in total stand volume between any of the treatments and the control. Shrub cover was significantly higher in the low and high alder treatments relative to the control, but there were no differences in shrub cover between birch treatments and the control. The results suggest that low alder density provides a good balance of conifer yield and understory development.
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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) | 0.001 | 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".