Challenges and opportunities in studies of host-pathogen interactions in forest tree species
Bibliographic record
Abstract
Root pathogens and rust diseases can cause extensive damage to Canadian forest tree species. Loss of growth, although difficult to visualize, is substantial over the life of a tree. Understanding hostpathogen interactions is important in managing yield loss and can aid in the identification of disease-resistant trees. However, studying the interactions involving forest pathogens offers both challenges and opportunities. Some of these issues are described from our perspective through working on white pines blister rust pathosystem and pathogens that infect the roots. Several resistance mechanisms to the white pine blister rust fungus, Cronartium ribicola, have been identified in pine. At the molecular level, several defence responsive proteins and their genes have been characterized. Some of these are identified to be potential candidates for markers associated with resistance or susceptibility. Current research activities and future directions and application of technologies to isolate and characterize resistance genes in white pine are discussed.Key words : white pine blister rust, Cronartium ribicola, root pathogens, pathosystem, marker proteins, resistance genes.
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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 | 0.027 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".