Effects of tree stump treatments for reducing <i>Armillaria</i> root disease within conifer plantations of southeastern British Columbia
Bibliographic record
Abstract
Root disease caused by the fungal pathogen, Armillaria solidipes, is commonly managed for in tree plantations of southeastern British Columbia by removing stumps before planting trees. The potential biocontrol, Hypholoma fasciculare, has been applied less commonly. We summarize the effects of mechanical stump removal and Hypholoma treatments on the development of 13 experimental conifer plantations aged 13–43 years consisting primarily of Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco), lodgepole pine ( Pinus contorta Douglas ex Loudon), and western larch ( Larix occidentalis Nutt.). Generalized additive models were used in the Akaike information criterion model selection with response variables chronologized for each treatment unit: Armillaria incidence, survivorship, basal diameter, diameter at breast height, height, and basal area index. Stump removals reduced Armillaria incidence in the oldest stands from 3.3% to 1.1%, increased survivorship from 30.3% to 69.7%, and produced 1.72 times greater basal area index. Armillaria reduction with stump removals was greater on Douglas-fir and western larch. At year 21, Hypholoma reduced Armillaria occurrence by 2.1 percentage points, whereas stump removals decreased Armillaria by 1.7 percentage points. Treatment decisions need to weigh silvicultural gains with financial and environmental costs, while Hypholoma treatments warrant expanded research.
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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 | 0.001 | 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".