Conifer growth, <i>Armillaria ostoyae</i> root disease, and plant diversity responses to broadleaf competition reduction in mixed forests of southern interior British Columbia
Bibliographic record
Abstract
Broadleaf trees are routinely removed from conifer plantations during vegetation management treatments, but whether the removal increases tree productivity or affects root disease and plant diversity is unknown. The effects of manual and chemical reduction of paper birch (Betula papyrifera Marsh.) and trembling aspen (Populus tremuloides Michx.) on conifer survival, growth, root disease incidence, and plant community diversity were investigated for 5 years in Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) plantations in southern interior British Columbia. Broadleaves were reduced by manual, girdling, and cut-stump glyphosate treatments for 5 years but most severely following cut-stump glyphosate and with a delay due to slow death following girdling. Conifer survival was reduced for 35 years following manual cutting or girdling of birch because of a 1.5- to 4-fold increase in mortality due to Armillaria ostoyae (Romagn.) Herink, but this did not occur following cut-stump glyphosate treatment of birch or manual cutting of aspen. Conifer diameter increased with treatment intensity and productivity of the vegetation complex. Competition thresholds were identified for diameter but not survival, although Armillaria-caused mortality tended to increase near the minimum growth threshold. Structural diversity increased following manual cutting and cut-stump glyphosate because birch dominants were removed and understory layers increased, but species richness and diversity were unaffected. Forest managers can expect increased conifer growth with birch removal but also small increases in mortality due to Armillaria ostoyae root disease following manual treatments and loss of large birch trees in all treatments.
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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 | 0.001 | 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".