Molecular tools in the study of the white pine blister rust [ <i>Cronartium ribicola</i> ] pathosystem
Bibliographic record
Abstract
Tree diseases cause extensive damage to Canadian forests, resulting in severe economic impact. Understanding host–pathogen interactions is important in managing yield loss and can aid in identification of disease-resistant trees. Molecular characterization of the genes and proteins that make up resistance and virulence phenotypes is critical to understand the function, evolution, and stability of a given conifer pathosystem. In this review, a perspective of some of these issues and challenges faced while working on the white pine blister rust [Cronartium ribicola] pathosystem is presented. Several defense-response proteins and their genes have been characterized through the use of proteomic and genomic approaches and may serve as candidates for markers associated with resistance or susceptibility to disease in white pine. Current research as well as future directions and application of technologies to isolate and characterize resistance genes in white pine are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".