Genetic Resistance to Fusiform Rust in Southern Pines and White Pine Blister Rust in White Pines—A Contrasting Tale of Two Rust Pathosystems—Current Status and Future Prospects
Bibliographic record
Abstract
Damage or mortality from pathogens can reduce productivity of forest plantations, as well as significantly harm natural forest ecosystems. Genetic resistance within the host species is the first line of defense for tree species. Resistance breeding programs for the native fusiform rust and exotic (to North America) white pine blister rust diseases are two of the longest concerted efforts in forest trees, spanning more than 50 years. Advances in developing greater genetic resistance have been made in both pathosystems, but unique challenges and opportunities in each system translate to different approaches. Fusiform rust resistance programs have mainly emphasized complete resistance, while partial resistance plays a prominent role in white pine blister rust resistance programs. Advances in the development of molecular genetic tools now permit investigations in conifers and their associated rust pathogens. Good progress has been made in identifying resistant populations and understanding resistance in these pathosystems, and resistant stock is now being used extensively for reforestation and restoration. These programs represent great success stories brought to fruition by the long-term efforts. However, continued support will be needed to enhance the level and fully realize the potential of durable genetic resistance in these invaluable North American conifer species.
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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 | 0.000 | 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 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".