Relationship between spruce beetle and tomentosus root disease: two natural disturbance agents of spruce
Bibliographic record
Abstract
This project investigated the interaction between tomentosus root disease of spruce, caused by Inonotus tomentosus (Fr.:Fr.) S. Teng, and spruce beetle (Dendroctonus rufipennis (Kirby)). Both organisms are important agents of mortality and volume loss in boreal and sub-boreal spruce forests of British Columbia. They also occur in similar stand types with respect to species composition and tree age. One study involved an intensive survey of 23 spruce stands, where trees were sampled for both beetle and root disease. Tree condition (dead standing, live, windthrown) was also recorded. Few stands showed a significant relationship between incidence of spruce beetle and incidence of root disease, regardless of tree condition. Observations indicated that beetles actually tended to avoid severely infected trees. A second study involved pheromone baiting of paired healthy and infected trees, and measurements of phloem thickness. Two sites were used, one with very high (epidemic) populations of beetles, and the second with low (endemic) levels. Spruce beetle attacks were more successful on infected trees compared with healthy trees only at the site with endemic levels of beetle. Collectively, the results indicate that tomentosus root disease helps to maintain endemic levels of spruce beetle, and disease incidence may be useful as a tool to identify areas that may have endemic populations of spruce beetle.
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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 | 0.000 |
| 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".