Effects of thinning paper birch on conifer productivity and understory plant diversity
Bibliographic record
Abstract
Naturally regenerated paper birch (Betula papyrifera Marsh.) is commonly removed from juvenile interior Douglas-fir (Pseudotsuga menziesii var. glauca [Beissn.] Franco) plantations in southern interior British Columbia, Canada, to increase conifer productivity and create a free-growing stand; however, this practice is expensive and contentious because of possible negative ecological impacts. One solution is to retain an optimal density of birch where growth gains of understory Douglas-fir are balanced against losses to Armillaria ostoyae (Romagn.) Herink and understory plant species diversity. We sought to find this optimal density by comparing four evenly applied birch density reduction treatments (0, 400, 1111, and 4444 retained birch stems ha−1) and an unthinned control (>7300 retained birch stems ha−1). The mortality rate of Douglas-fir due to Armillaria root disease increased non-significantly with thinning intensity. Mean diameter increment of surviving Douglas-fir improved the most where birch was completely removed, with little variation among intermediate thinning treatments. Height growth was unaffected by the thinning treatments. Diversity of cryptogams was significantly greater in the control than where all birch was removed. We suggest that the treatment with 4444 retained birch stems ha−1 provides the best balance for improving Douglas-fir growth while minimizing risk of increased Armillaria root disease and reduced understory plant diversity in young mixed 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 | 0.000 | 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 | 0.000 | 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".