Effects of selective cutting on the epidemiology of armillaria root disease in the southern interior of British Columbia
Bibliographic record
Abstract
In selectively cut and undisturbed parts of four mature stands, five 0.04-ha plots were established, and trees were measured, mapped, and examined for aboveground symptoms of armillaria root disease. Trees were felled, and stumps and their root systems were removed by an excavator and were measured and examined for Armillaria lesions. Isolates from root lesions, rhizomorphs associated with lesions, and basidiomes collected in or adjacent to plots were of Armillaria ostoyae (Romagn.) Herink. All trees were assigned to one of five tree condition classes based on the location of lesions and host response. The merchantable volume in each class was calculated. In undisturbed plots, incidence of trees with A. ostoyae lesions on roots was about 10% in the dry climatic region compared with about 75% in the moist region and 35% in the wet region. In plots in the selectively cut parts of the stands, 50-100% of stumps were colonized by A. ostoyae. Results of a logistic regression analysis showed that selective cutting was associated with a statistically significant increase in the probability of a tree having A. ostoyae lesions, where the magnitude of the increase depended on tree diameter. The increase in the probability of a tree being diseased was accompanied by an increase in the proportion of primary roots with lesions and the average number of lesions per diseased tree; however, the increases in disease intensity were statistically significant at only two (one dry and one moist) of the four sites. The percentage of merchantable volume threatened or killed by A. ostoyae was usually higher in cutover than undisturbed plots.
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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 | 0.001 |
| 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".