Progress on Molecular Genetics and Manipulation of Rust Fungi
Bibliographic record
Abstract
Among the thousands of rust species described, many are known for their devastating effects on their hosts, which include major agriculture crops and trees. Hence, for over a century, these basidiomycete pathogenic fungi have been researched and experimented with. However, due to their biotrophic nature, they are challenging organisms to work with and, needing their hosts for propagation, represent pathosystems that are not easily experimentally accessible. Indeed, efforts to perform genetics have been few and far apart for the rust fungi, though one study performed in the 1940s was famously instrumental in formulating the gene-for-gene hypothesis describing pathogen-host interactions. By taking full advantage of the molecular genetic tools developed in the 1980s, research on many plant pathogenic microbes thrived, yet similar work on the rusts remained very challenging though not without some successes. However, the genomics era brought real breakthrough research for the biotrophic fungi and with innovative experimentation and the use of heterologous systems, molecular genetic analyses over the last 2 decades have significantly advanced our insight into the function of many rust fungus genes and their role in the interaction with their hosts. This has allowed optimizing efforts for resistance breeding and the design and testing of various novel strategies to reduce the devastating diseases they cause.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".