Selective removal of paper birch increases growth of juvenile Douglas-fir while minimizing impacts on the plant community
Bibliographic record
Abstract
Broadcast weeding of naturally regenerating paper birch (Betula papyrifera) to improve performance of conifer crop trees is common in forest plantations in British Columbia, Canada, but such treatments are costly, reduce biodiversity and may increase disease. To investigate whether alternative treatments can enhance conifer performance with minimal loss of other ecosystem services, we examined effects of selective removal of paper birch in two interior Douglas-fir (Pseudotsuga menziesii var. glauca) plantations on Douglas-fir growth and survival, and cover and height of residual birch and the understorey plant community. Douglas-fir-centred birch-free patches of varying radius (0–2.5 m) were created by single and repeated manual cutting and Triclopyr® application. The “threshold brushing radius,” defined as the birch removal radius at which Douglas-fir performance was optimized was 2.0 m at the more productive site and 1.5 m at the less productive one. Armillaria root disease, the major Douglas-fir mortality agent, spiked in the 1.0 m radius manual cutting treatment at one location. The greatest reductions in birch and understory plant abundance occurred following the most intensive treatments. Selective brushing treatments are an effective alternative to broadcast removal of paper birch for increasing conifer performance, and have less impact on the plant community.
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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".